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ultimatevocalremovergui/inference_MDX.py
Anjok07 7778d4c3fb
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2022-08-23 15:04:39 -05:00

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from datetime import datetime
from demucs.apply import BagOfModels, apply_model
from demucs.hdemucs import HDemucs
from demucs.model_v2 import Demucs
from demucs.pretrained import get_model as _gm
from demucs.tasnet_v2 import ConvTasNet
from demucs.utils import apply_model_v1
from demucs.utils import apply_model_v2
from lib_v5 import spec_utils
from lib_v5.model_param_init import ModelParameters
from models import get_models, spec_effects
from pathlib import Path
from random import randrange
from tqdm import tqdm
from unittest import skip
import tkinter.ttk as ttk
import tkinter.messagebox
import tkinter.filedialog
import tkinter.simpledialog
import tkinter.font
import tkinter as tk
from tkinter import *
from tkinter.tix import *
import json
import gzip
import hashlib
import librosa
import numpy as np
import onnxruntime as ort
import os
import os.path
import pathlib
import psutil
import pydub
import shutil
import soundfile as sf
import subprocess
from UVR import MainWindow
import sys
import time
import time # Timer
import tkinter as tk
import torch
import traceback # Error Message Recent Calls
import warnings
import lib_v5.filelist
#from typing import Literal
class Predictor():
def __init__(self):
pass
def mdx_options(self):
"""
Open Advanced MDX Options
"""
self.okVar = tk.IntVar()
self.n_fft_scale_set_var = tk.StringVar(value='6144')
self.dim_f_set_var = tk.StringVar(value='2048')
self.mdxnetModeltype_var = tk.StringVar(value='Vocals')
self.noise_pro_select_set_var = tk.StringVar(value='MDX-NET_Noise_Profile_14_kHz')
self.compensate_v_var = tk.StringVar(value=1.03597672895)
mdx_model_set = Toplevel()
mdx_model_set.geometry("490x515")
window_height = 490
window_width = 515
mdx_model_set.title("Specify Parameters")
mdx_model_set.resizable(False, False) # This code helps to disable windows from resizing
mdx_model_set.attributes("-topmost", True)
screen_width = mdx_model_set.winfo_screenwidth()
screen_height = mdx_model_set.winfo_screenheight()
x_cordinate = int((screen_width/2) - (window_width/2))
y_cordinate = int((screen_height/2) - (window_height/2))
mdx_model_set.geometry("{}x{}+{}+{}".format(window_width, window_height, x_cordinate, y_cordinate))
x = main_window.winfo_x()
y = main_window.winfo_y()
mdx_model_set.geometry("+%d+%d" %(x+50,y+150))
mdx_model_set.wm_transient(main_window)
# change title bar icon
mdx_model_set.iconbitmap('img\\UVR-Icon-v2.ico')
mdx_model_set_window = ttk.Notebook(mdx_model_set)
mdx_model_set_window.pack(expand = 1, fill ="both")
mdx_model_set_window.grid_rowconfigure(0, weight=1)
mdx_model_set_window.grid_columnconfigure(0, weight=1)
frame0=Frame(mdx_model_set_window,highlightbackground='red',highlightthicknes=0)
frame0.grid(row=0,column=0,padx=0,pady=0)
#frame0.tkraise(frame0)
space_small = ' '*20
space_small_1 = ' '*10
l0=tk.Label(frame0, text=f'\n{space_small}Stem Type{space_small}', font=("Century Gothic", "9"), foreground='#13a4c9')
l0.grid(row=3,column=0,padx=0,pady=5)
l0=ttk.OptionMenu(frame0, self.mdxnetModeltype_var, None, 'Vocals', 'Instrumental', 'Other', 'Bass', 'Drums')
l0.grid(row=4,column=0,padx=0,pady=5)
l0=tk.Label(frame0, text='N_FFT Scale', font=("Century Gothic", "9"), foreground='#13a4c9')
l0.grid(row=5,column=0,padx=0,pady=5)
l0=tk.Label(frame0, text=f'{space_small_1}(Manual Set){space_small_1}', font=("Century Gothic", "9"), foreground='#13a4c9')
l0.grid(row=5,column=1,padx=0,pady=5)
self.options_n_fft_scale_Opt = l0=ttk.OptionMenu(frame0, self.n_fft_scale_set_var, None, '4096', '6144', '7680', '8192', '16384')
self.options_n_fft_scale_Opt
l0.grid(row=6,column=0,padx=0,pady=5)
self.options_n_fft_scale_Entry = l0=ttk.Entry(frame0, textvariable=self.n_fft_scale_set_var, justify='center')
self.options_n_fft_scale_Entry
l0.grid(row=6,column=1,padx=0,pady=5)
l0=tk.Label(frame0, text='Dim_f', font=("Century Gothic", "9"), foreground='#13a4c9')
l0.grid(row=7,column=0,padx=0,pady=5)
l0=tk.Label(frame0, text='(Manual Set)', font=("Century Gothic", "9"), foreground='#13a4c9')
l0.grid(row=7,column=1,padx=0,pady=5)
self.options_dim_f_Opt = l0=ttk.OptionMenu(frame0, self.dim_f_set_var, None, '2048', '3072', '4096')
self.options_dim_f_Opt
l0.grid(row=8,column=0,padx=0,pady=5)
self.options_dim_f_Entry = l0=ttk.Entry(frame0, textvariable=self.dim_f_set_var, justify='center')
self.options_dim_f_Entry
l0.grid(row=8,column=1,padx=0,pady=5)
l0=tk.Label(frame0, text='Noise Profile', font=("Century Gothic", "9"), foreground='#13a4c9')
l0.grid(row=9,column=0,padx=0,pady=5)
l0=ttk.OptionMenu(frame0, self.noise_pro_select_set_var, None, 'MDX-NET_Noise_Profile_14_kHz', 'MDX-NET_Noise_Profile_17_kHz', 'MDX-NET_Noise_Profile_Full_Band')
l0.grid(row=10,column=0,padx=0,pady=5)
l0=tk.Label(frame0, text='Volume Compensation', font=("Century Gothic", "9"), foreground='#13a4c9')
l0.grid(row=11,column=0,padx=0,pady=10)
self.options_compensate = l0=ttk.Entry(frame0, textvariable=self.compensate_v_var, justify='center')
self.options_compensate
l0.grid(row=12,column=0,padx=0,pady=0)
l0=ttk.Button(frame0,text="Continue", command=lambda: self.okVar.set(1))
l0.grid(row=13,column=0,padx=0,pady=30)
def stop():
widget_text.write(f'Please configure the ONNX model settings accordingly and try again.\n\n')
widget_text.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
torch.cuda.empty_cache()
gui_progress_bar.set(0)
widget_button.configure(state=tk.NORMAL) # Enable Button
self.okVar.set(1)
stop_button()
mdx_model_set.destroy()
return
l0=ttk.Button(frame0,text="Stop Process", command=stop)
l0.grid(row=13,column=1,padx=0,pady=30)
mdx_model_set.protocol("WM_DELETE_WINDOW", stop)
frame0.wait_variable(self.okVar)
global n_fft_scale_set
global dim_f_set
global modeltype
global stemset_n
global stem_text_a
global stem_text_b
global source_val
global noise_pro_set
global compensate
global demucs_model_set
stemtype = self.mdxnetModeltype_var.get()
if stemtype == 'Vocals':
modeltype = 'v'
stemset_n = '(Vocals)'
source_val = 3
if stemtype == 'Instrumental':
modeltype = 'v'
stemset_n = '(Instrumental)'
source_val = 0
if stemtype == 'Other':
modeltype = 'o'
stemset_n = '(Other)'
source_val = 2
if stemtype == 'Drums':
modeltype = 'd'
stemset_n = '(Drums)'
source_val = 1
if stemtype == 'Bass':
modeltype = 'b'
stemset_n = '(Bass)'
source_val = 0
if stemset_n == '(Vocals)':
stem_text_a = 'Vocals'
stem_text_b = 'Instrumental'
elif stemset_n == '(Instrumental)':
stem_text_a = 'Instrumental'
stem_text_b = 'Vocals'
elif stemset_n == '(Other)':
stem_text_a = 'Other'
stem_text_b = 'mixture without selected stem'
elif stemset_n == '(Drums)':
stem_text_a = 'Drums'
stem_text_b = 'mixture without selected stem'
elif stemset_n == '(Bass)':
stem_text_a = 'Bass'
stem_text_b = 'mixture without selected stem'
else:
stem_text_a = 'Vocals'
stem_text_b = 'Instrumental'
compensate = self.compensate_v_var.get()
n_fft_scale_set = int(self.n_fft_scale_set_var.get())
dim_f_set = int(self.dim_f_set_var.get())
noise_pro_set = self.noise_pro_select_set_var.get()
mdx_model_params = {
'modeltype' : modeltype,
'stemset_n' : stemset_n,
'source_val' : source_val,
'compensate' : compensate,
'n_fft_scale_set' : n_fft_scale_set,
'dim_f_set' : dim_f_set,
'noise_pro' : noise_pro_set,
}
mdx_model_params_r = json.dumps(mdx_model_params, indent=4)
with open(f"lib_v5/filelists/model_cache/mdx_model_cache/{model_hash}.json", "w") as outfile:
outfile.write(mdx_model_params_r)
if 'UVR' in demucs_model_set:
if stemset_n == '(Bass)' or stemset_n == '(Drums)' or stemset_n == '(Other)':
widget_text.write(base_text + 'The selected Demucs model can only be used with vocal or instrumental stems.\n')
widget_text.write(base_text + 'Please select a 4 stem Demucs model next time.\n')
widget_text.write(base_text + 'Setting Demucs Model to \"mdx_extra\"\n')
demucs_model_set = 'mdx_extra'
if stemset_n == '(Instrumental)':
if not 'UVR' in demucs_model_set:
widget_text.write(base_text + 'The selected Demucs model cannot be used with this model.\n')
widget_text.write(base_text + 'Only 2 stem Demucs models are compatible with this model.\n')
widget_text.write(base_text + 'Setting Demucs model to \"UVR_Demucs_Model_1\".\n\n')
demucs_model_set = 'UVR_Demucs_Model_1'
mdx_model_set.destroy()
def prediction_setup(self):
global device
if data['gpu'] >= 0:
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
if data['gpu'] == -1:
device = torch.device('cpu')
if data['demucsmodel']:
if demucs_model_version == 'v1':
load_from = "models/Demucs_Models/"f"{demucs_model_set}"
if str(load_from).endswith(".gz"):
load_from = gzip.open(load_from, "rb")
klass, args, kwargs, state = torch.load(load_from)
self.demucs = klass(*args, **kwargs)
widget_text.write(base_text + 'Loading Demucs v1 model... ')
update_progress(**progress_kwargs,
step=0.05)
self.demucs.to(device)
self.demucs.load_state_dict(state)
widget_text.write('Done!\n')
if not data['segment'] == 'Default':
widget_text.write(base_text + 'Segments is only available in Demucs v3. Please use \"Chunks\" instead.\n')
else:
pass
if demucs_model_version == 'v2':
if '48' in demucs_model_set:
channels=48
elif 'unittest' in demucs_model_set:
channels=4
else:
channels=64
if 'tasnet' in demucs_model_set:
self.demucs = ConvTasNet(sources=["drums", "bass", "other", "vocals"], X=10)
else:
self.demucs = Demucs(sources=["drums", "bass", "other", "vocals"], channels=channels)
widget_text.write(base_text + 'Loading Demucs v2 model... ')
update_progress(**progress_kwargs,
step=0.05)
self.demucs.to(device)
self.demucs.load_state_dict(torch.load("models/Demucs_Models/"f"{demucs_model_set}"))
widget_text.write('Done!\n')
if not data['segment'] == 'Default':
widget_text.write(base_text + 'Segments is only available in Demucs v3. Please use \"Chunks\" instead.\n')
else:
pass
self.demucs.eval()
if demucs_model_version == 'v3':
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/v3_repo')
#print('What Demucs model was chosen? ', demucs_model_set)
self.demucs = _gm(name=demucs_model_set, repo=path_d)
self.demucs.to(device)
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")
if data['segment'] == 'Default':
segment = None
if isinstance(self.demucs, BagOfModels):
if segment is not None:
for sub in self.demucs.models:
sub.segment = segment
else:
if segment is not None:
sub.segment = segment
else:
try:
segment = int(data['segment'])
if isinstance(self.demucs, BagOfModels):
if segment is not None:
for sub in self.demucs.models:
sub.segment = segment
else:
if segment is not None:
sub.segment = segment
if split_mode:
widget_text.write(base_text + "Segments set to "f"{segment}.\n")
except:
segment = None
if isinstance(self.demucs, BagOfModels):
if segment is not None:
for sub in self.demucs.models:
sub.segment = segment
else:
if segment is not None:
sub.segment = segment
self.onnx_models = {}
c = 0
self.models = get_models('tdf_extra', load=False, device=cpu, stems=modeltype, n_fft_scale=int(n_fft_scale_set), dim_f=int(dim_f_set))
if not data['demucs_only']:
widget_text.write(base_text + 'Loading ONNX model... ')
update_progress(**progress_kwargs,
step=0.1)
c+=1
if data['gpu'] >= 0:
if torch.cuda.is_available():
run_type = ['CUDAExecutionProvider']
else:
data['gpu'] = -1
widget_text.write("\n" + base_text + "No NVIDIA GPU detected. Switching to CPU... ")
run_type = ['CPUExecutionProvider']
elif data['gpu'] == -1:
run_type = ['CPUExecutionProvider']
print('Selected Model: ', mdx_model_path)
self.onnx_models[c] = ort.InferenceSession(os.path.join(mdx_model_path), providers=run_type)
if not data['demucs_only']:
widget_text.write('Done!\n')
def prediction(self, m):
mix, samplerate = librosa.load(m, mono=False, sr=44100)
#print('print mix: ', mix)
if mix.ndim == 1:
mix = np.asfortranarray([mix,mix])
samplerate = samplerate
mix = mix.T
sources = self.demix(mix.T)
widget_text.write(base_text + 'Inferences complete!\n')
c = -1
#Main Save Path
save_path = os.path.dirname(_basename)
inst_only = data['inst_only']
voc_only = data['voc_only']
#print('stemset_n: ', stemset_n)
if stemset_n == '(Instrumental)':
if data['inst_only'] == True:
voc_only = True
inst_only = False
if data['voc_only'] == True:
inst_only = True
voc_only = False
#Vocal Path
if stemset_n == '(Vocals)':
vocal_name = '(Vocals)'
elif stemset_n == '(Instrumental)':
vocal_name = '(Instrumental)'
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,
file_name = f'{os.path.basename(_basename)}_{vocal_name}_{mdx_model_name}',)
vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{vocal_name}_{mdx_model_name}',)
vocal_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{vocal_name}_{mdx_model_name}',)
else:
vocal_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{vocal_name}',)
vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{vocal_name}',)
vocal_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{vocal_name}',)
#Instrumental Path
if stemset_n == '(Vocals)':
Instrumental_name = '(Instrumental)'
elif stemset_n == '(Instrumental)':
Instrumental_name = '(Vocals)'
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,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{mdx_model_name}',)
Instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{mdx_model_name}',)
Instrumental_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{mdx_model_name}',)
else:
Instrumental_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}',)
Instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}',)
Instrumental_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}',)
#Non-Reduced Vocal Path
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)'
elif stemset_n == '(Instrumental)':
vocal_name = '(Instrumental)'
if data['modelFolder']:
non_reduced_vocal_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{vocal_name}_{mdx_model_name}_No_Reduction',)
non_reduced_vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{vocal_name}_{mdx_model_name}_No_Reduction',)
non_reduced_vocal_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{vocal_name}_{mdx_model_name}_No_Reduction',)
else:
non_reduced_vocal_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{vocal_name}_No_Reduction',)
non_reduced_vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{vocal_name}_No_Reduction',)
non_reduced_vocal_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{vocal_name}_No_Reduction',)
if data['modelFolder']:
non_reduced_Instrumental_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{mdx_model_name}_No_Reduction',)
non_reduced_Instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{mdx_model_name}_No_Reduction',)
non_reduced_Instrumental_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{mdx_model_name}_No_Reduction',)
else:
non_reduced_Instrumental_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_No_Reduction',)
non_reduced_Instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_No_Reduction',)
non_reduced_Instrumental_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_No_Reduction',)
if os.path.isfile(non_reduced_vocal_path):
file_exists_n = 'there'
else:
file_exists_n = 'not_there'
if os.path.isfile(vocal_path):
file_exists_v = 'there'
else:
file_exists_v = 'not_there'
if os.path.isfile(Instrumental_path):
file_exists_i = 'there'
else:
file_exists_i = 'not_there'
#print('Is there already a voc file there? ', file_exists_v)
if not data['noisereduc_s'] == 'None':
c += 1
if not data['demucsmodel']:
if inst_only:
widget_text.write(base_text + f'Preparing to save {stem_text_b}...')
else:
widget_text.write(base_text + f'Saving {stem_text_a}... ')
sf.write(non_reduced_vocal_path, sources[c].T, samplerate, subtype=wav_type_set)
update_progress(**progress_kwargs,
step=(0.9))
widget_text.write('Done!\n')
widget_text.write(base_text + 'Performing Noise Reduction... ')
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\\" + noise_pro_set + ".prof " + f"{reduction_sen}",
shell=True, stdout=subprocess.PIPE,
stdin=subprocess.PIPE, stderr=subprocess.PIPE)
widget_text.write('Done!\n')
update_progress(**progress_kwargs,
step=(0.95))
else:
if inst_only:
widget_text.write(base_text + f'Preparing {stem_text_b}...')
else:
widget_text.write(base_text + f'Saving {stem_text_a}... ')
if data['demucs_only']:
if 'UVR' in demucs_model_set:
if stemset_n == '(Instrumental)':
sf.write(non_reduced_vocal_path, sources[0].T, samplerate, subtype=wav_type_set)
else:
sf.write(non_reduced_vocal_path, sources[1].T, samplerate, subtype=wav_type_set)
else:
sf.write(non_reduced_vocal_path, sources[source_val].T, samplerate, subtype=wav_type_set)
update_progress(**progress_kwargs,
step=(0.9))
widget_text.write('Done!\n')
widget_text.write(base_text + 'Performing Noise Reduction... ')
reduction_sen = float(data['noisereduc_s'])/10
#print(noise_pro_set)
subprocess.call("lib_v5\\sox\\sox.exe" + ' "' +
f"{str(non_reduced_vocal_path)}" + '" "' + f"{str(vocal_path)}" + '" ' +
"noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}",
shell=True, stdout=subprocess.PIPE,
stdin=subprocess.PIPE, stderr=subprocess.PIPE)
update_progress(**progress_kwargs,
step=(0.95))
widget_text.write('Done!\n')
else:
c += 1
if not data['demucsmodel']:
if inst_only:
widget_text.write(base_text + f'Preparing {stem_text_b}...')
else:
widget_text.write(base_text + f'Saving {stem_text_a}... ')
sf.write(vocal_path, sources[c].T, samplerate, subtype=wav_type_set)
update_progress(**progress_kwargs,
step=(0.9))
widget_text.write('Done!\n')
else:
if inst_only:
widget_text.write(base_text + f'Preparing {stem_text_b}...')
else:
widget_text.write(base_text + f'Saving {stem_text_a}... ')
if data['demucs_only']:
if 'UVR' in demucs_model_set:
if stemset_n == '(Instrumental)':
sf.write(vocal_path, sources[0].T, samplerate, subtype=wav_type_set)
else:
sf.write(vocal_path, sources[1].T, samplerate, subtype=wav_type_set)
else:
sf.write(vocal_path, sources[source_val].T, samplerate, subtype=wav_type_set)
else:
sf.write(vocal_path, sources[source_val].T, samplerate, subtype=wav_type_set)
update_progress(**progress_kwargs,
step=(0.9))
widget_text.write('Done!\n')
if voc_only and not inst_only:
pass
else:
if not data['noisereduc_s'] == 'None':
if data['nophaseinst']:
finalfiles = [
{
'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json',
'files':[str(music_file), non_reduced_vocal_path],
}
]
else:
finalfiles = [
{
'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json',
'files':[str(music_file), vocal_path],
}
]
else:
finalfiles = [
{
'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json',
'files':[str(music_file), vocal_path],
}
]
widget_text.write(base_text + f'Saving {stem_text_b}... ')
for i, e in tqdm(enumerate(finalfiles)):
wave, specs = {}, {}
mp = ModelParameters(e['model_params'])
for i in range(len(e['files'])):
spec = {}
for d in range(len(mp.param['band']), 0, -1):
bp = mp.param['band'][d]
if d == len(mp.param['band']): # high-end band
wave[d], _ = librosa.load(
e['files'][i], bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
if len(wave[d].shape) == 1: # mono to stereo
wave[d] = np.array([wave[d], wave[d]])
else: # lower bands
wave[d] = librosa.resample(wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
spec[d] = spec_utils.wave_to_spectrogram(wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse'])
specs[i] = spec_utils.combine_spectrograms(spec, mp)
del wave
ln = min([specs[0].shape[2], specs[1].shape[2]])
specs[0] = specs[0][:,:,:ln]
specs[1] = specs[1][:,:,:ln]
X_mag = np.abs(specs[0])
y_mag = np.abs(specs[1])
max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
v_spec = specs[1] - max_mag * np.exp(1.j * np.angle(specs[0]))
update_progress(**progress_kwargs,
step=(1))
if not data['noisereduc_s'] == 'None':
if data['nophaseinst']:
sf.write(non_reduced_Instrumental_path, normalization_set(spec_utils.cmb_spectrogram_to_wave(-v_spec, mp)), mp.param['sr'], subtype=wav_type_set)
reduction_sen = float(data['noisereduc_s'])/10
#print(noise_pro_set)
subprocess.call("lib_v5\\sox\\sox.exe" + ' "' +
f"{str(non_reduced_Instrumental_path)}" + '" "' + f"{str(Instrumental_path)}" + '" ' +
"noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}",
shell=True, stdout=subprocess.PIPE,
stdin=subprocess.PIPE, stderr=subprocess.PIPE)
else:
sf.write(Instrumental_path, normalization_set(spec_utils.cmb_spectrogram_to_wave(-v_spec, mp)), mp.param['sr'], subtype=wav_type_set)
else:
sf.write(Instrumental_path, normalization_set(spec_utils.cmb_spectrogram_to_wave(-v_spec, mp)), mp.param['sr'], subtype=wav_type_set)
if inst_only:
if file_exists_v == 'there':
pass
else:
try:
os.remove(vocal_path)
except:
pass
widget_text.write('Done!\n')
if data['saveFormat'] == 'Mp3':
try:
if inst_only == True:
if data['non_red'] == True:
if not data['nophaseinst']:
pass
else:
musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path)
musfile.export(non_reduced_Instrumental_path_mp3, format="mp3", bitrate=mp3_bit_set)
try:
os.remove(non_reduced_Instrumental_path)
except:
pass
pass
else:
musfile = pydub.AudioSegment.from_wav(vocal_path)
musfile.export(vocal_path_mp3, format="mp3", bitrate=mp3_bit_set)
if file_exists_v == 'there':
pass
else:
try:
os.remove(vocal_path)
except:
pass
if data['non_red'] == True:
if not data['nophaseinst']:
pass
else:
if voc_only == True:
pass
else:
musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path)
musfile.export(non_reduced_Instrumental_path_mp3, format="mp3", bitrate=mp3_bit_set)
if file_exists_n == 'there':
pass
else:
try:
os.remove(non_reduced_Instrumental_path)
except:
pass
if voc_only == True:
if data['non_red'] == True:
musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path)
musfile.export(non_reduced_vocal_path_mp3, format="mp3", bitrate=mp3_bit_set)
try:
os.remove(non_reduced_vocal_path)
except:
pass
pass
else:
musfile = pydub.AudioSegment.from_wav(Instrumental_path)
musfile.export(Instrumental_path_mp3, format="mp3", bitrate=mp3_bit_set)
if file_exists_i == 'there':
pass
else:
try:
os.remove(Instrumental_path)
except:
pass
if data['non_red'] == True:
if inst_only == True:
pass
else:
musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path)
musfile.export(non_reduced_vocal_path_mp3, format="mp3", bitrate=mp3_bit_set)
if file_exists_n == 'there':
pass
else:
try:
os.remove(non_reduced_vocal_path)
except:
pass
except Exception as e:
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n'
if "ffmpeg" in errmessage:
widget_text.write(base_text + 'Failed to save output(s) as Mp3(s).\n')
widget_text.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n')
widget_text.write(base_text + 'Moving on...\n')
else:
widget_text.write(base_text + 'Failed to save output(s) as Mp3(s).\n')
widget_text.write(base_text + 'Please check error log.\n')
widget_text.write(base_text + 'Moving on...\n')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while attempting to save file as mp3 "{os.path.basename(music_file)}":\n\n' +
f'Process Method: MDX-Net\n\n' +
f'FFmpeg might be missing or corrupted.\n\n' +
f'If this error persists, please contact the developers.\n\n' +
f'Raw error details:\n\n' +
errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
if data['saveFormat'] == 'Flac':
try:
if inst_only == True:
if data['non_red'] == True:
if not data['nophaseinst']:
pass
else:
musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path)
musfile.export(non_reduced_Instrumental_path_flac, format="flac")
try:
os.remove(non_reduced_Instrumental_path)
except:
pass
pass
else:
musfile = pydub.AudioSegment.from_wav(vocal_path)
musfile.export(vocal_path_flac, format="flac")
if file_exists_v == 'there':
pass
else:
try:
os.remove(vocal_path)
except:
pass
if data['non_red'] == True:
if not data['nophaseinst']:
pass
else:
if voc_only == True:
pass
else:
musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path)
musfile.export(non_reduced_Instrumental_path_flac, format="flac")
if file_exists_n == 'there':
pass
else:
try:
os.remove(non_reduced_Instrumental_path)
except:
pass
if voc_only == True:
if data['non_red'] == True:
musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path)
musfile.export(non_reduced_vocal_path_flac, format="flac")
try:
os.remove(non_reduced_vocal_path)
except:
pass
pass
else:
musfile = pydub.AudioSegment.from_wav(Instrumental_path)
musfile.export(Instrumental_path_flac, format="flac")
if file_exists_i == 'there':
pass
else:
try:
os.remove(Instrumental_path)
except:
pass
if data['non_red'] == True:
if inst_only == True:
pass
else:
musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path)
musfile.export(non_reduced_vocal_path_flac, format="flac")
if file_exists_n == 'there':
pass
else:
try:
os.remove(non_reduced_vocal_path)
except:
pass
except Exception as e:
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n'
if "ffmpeg" in errmessage:
widget_text.write(base_text + 'Failed to save output(s) as Flac(s).\n')
widget_text.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n')
widget_text.write(base_text + 'Moving on...\n')
else:
widget_text.write(base_text + 'Failed to save output(s) as Flac(s).\n')
widget_text.write(base_text + 'Please check error log.\n')
widget_text.write(base_text + 'Moving on...\n')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while attempting to save file as flac "{os.path.basename(music_file)}":\n\n' +
f'Process Method: MDX-Net\n\n' +
f'FFmpeg might be missing or corrupted.\n\n' +
f'If this error persists, please contact the developers.\n\n' +
f'Raw error details:\n\n' +
errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
if data['noisereduc_s'] == 'None':
pass
elif data['non_red'] == True:
if inst_only:
if file_exists_n == 'there':
pass
else:
try:
os.remove(non_reduced_vocal_path)
except:
pass
pass
elif inst_only:
if file_exists_n == 'there':
pass
else:
try:
os.remove(non_reduced_vocal_path)
except:
pass
else:
try:
os.remove(non_reduced_vocal_path)
os.remove(non_reduced_Instrumental_path)
except:
pass
widget_text.write(base_text + 'Completed Separation!\n')
def demix(self, mix):
global chunk_set
# 1 = demucs only
# 0 = onnx only
if data['chunks'] == 'Full':
chunk_set = 0
widget_text.write(base_text + "Chunk size user-set to \"Full\"... \n")
elif data['chunks'] == 'Auto':
if data['gpu'] == 0:
try:
gpu_mem = round(torch.cuda.get_device_properties(0).total_memory/1.074e+9)
except:
widget_text.write(base_text + 'NVIDIA GPU Required for conversion!\n')
data['gpu'] = -1
pass
if int(gpu_mem) <= int(6):
chunk_set = int(5)
widget_text.write(base_text + 'Chunk size auto-set to 5... \n')
if gpu_mem in [7, 8, 9, 10, 11, 12, 13, 14, 15]:
chunk_set = int(10)
widget_text.write(base_text + 'Chunk size auto-set to 10... \n')
if int(gpu_mem) >= int(16):
chunk_set = int(40)
widget_text.write(base_text + 'Chunk size auto-set to 40... \n')
if data['gpu'] == -1:
sys_mem = psutil.virtual_memory().total >> 30
if int(sys_mem) <= int(4):
chunk_set = int(1)
widget_text.write(base_text + 'Chunk size auto-set to 1... \n')
if sys_mem in [5, 6, 7, 8]:
chunk_set = int(10)
widget_text.write(base_text + 'Chunk size auto-set to 10... \n')
if sys_mem in [9, 10, 11, 12, 13, 14, 15, 16]:
chunk_set = int(25)
widget_text.write(base_text + 'Chunk size auto-set to 25... \n')
if int(sys_mem) >= int(17):
chunk_set = int(60)
widget_text.write(base_text + 'Chunk size auto-set to 60... \n')
elif data['chunks'] == '0':
chunk_set = 0
widget_text.write(base_text + "Chunk size user-set to \"Full\"... \n")
else:
chunk_set = int(data['chunks'])
widget_text.write(base_text + "Chunk size user-set to "f"{chunk_set}... \n")
samples = mix.shape[-1]
margin = margin_set
chunk_size = chunk_set*44100
assert not margin == 0, 'margin cannot be zero!'
if margin > chunk_size:
margin = chunk_size
b = np.array([[[0.5]], [[0.5]], [[0.7]], [[0.9]]])
segmented_mix = {}
if chunk_set == 0 or samples < chunk_size:
chunk_size = samples
counter = -1
for skip in range(0, samples, chunk_size):
counter+=1
s_margin = 0 if counter == 0 else margin
end = min(skip+chunk_size+margin, samples)
start = skip-s_margin
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']:
if no_chunk_demucs == False:
sources = self.demix_demucs_split(mix)
if no_chunk_demucs == True:
sources = self.demix_demucs(segmented_mix, margin_size=margin)
else: # both, apply spec effects
base_out = self.demix_base(segmented_mix, margin_size=margin)
if demucs_model_version == 'v1':
if no_chunk_demucs == False:
demucs_out = self.demix_demucs_v1_split(mix)
if no_chunk_demucs == True:
demucs_out = self.demix_demucs_v1(segmented_mix, margin_size=margin)
if demucs_model_version == 'v2':
if no_chunk_demucs == False:
demucs_out = self.demix_demucs_v2_split(mix)
if no_chunk_demucs == True:
demucs_out = self.demix_demucs_v2(segmented_mix, margin_size=margin)
if demucs_model_version == 'v3':
if no_chunk_demucs == False:
demucs_out = self.demix_demucs_split(mix)
if no_chunk_demucs == True:
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'])
if 'UVR' in demucs_model_set:
if stemset_n == '(Instrumental)':
sources[source_val] = (spec_effects(wave=[demucs_out[0],base_out[0]],
algorithm=data['mixing'],
value=b[source_val])*float(compensate)) # compensation
else:
sources[source_val] = (spec_effects(wave=[demucs_out[1],base_out[0]],
algorithm=data['mixing'],
value=b[source_val])*float(compensate)) # compensation
else:
sources[source_val] = (spec_effects(wave=[demucs_out[source_val],base_out[0]],
algorithm=data['mixing'],
value=b[source_val])*float(compensate)) # compensation
if not data['demucsmodel']:
return sources*float(compensate)
else:
return sources
def demix_base(self, mixes, margin_size):
chunked_sources = []
onnxitera = len(mixes)
onnxitera_calc = onnxitera * 2
gui_progress_bar_onnx = 0
progress_bar = 0
print(' Running ONNX Inference...')
if onnxitera == 1:
widget_text.write(base_text + f"Running ONNX Inference... ")
else:
widget_text.write(base_text + f"Running ONNX Inference...{space}\n")
for mix in mixes:
gui_progress_bar_onnx += 1
if data['demucsmodel']:
update_progress(**progress_kwargs,
step=(0.1 + (0.5/onnxitera_calc * gui_progress_bar_onnx)))
else:
update_progress(**progress_kwargs,
step=(0.1 + (0.9/onnxitera * gui_progress_bar_onnx)))
progress_bar += 100
step = (progress_bar / onnxitera)
if onnxitera == 1:
pass
else:
percent_prog = f"{base_text}MDX-Net Inference Progress: {gui_progress_bar_onnx}/{onnxitera} | {round(step)}%"
widget_text.percentage(percent_prog)
cmix = mixes[mix]
sources = []
n_sample = cmix.shape[1]
mod = 0
for model in self.models:
mod += 1
trim = model.n_fft//2
gen_size = model.chunk_size-2*trim
pad = gen_size - n_sample%gen_size
mix_p = np.concatenate((np.zeros((2,trim)), cmix, np.zeros((2,pad)), np.zeros((2,trim))), 1)
mix_waves = []
i = 0
while i < n_sample + pad:
waves = np.array(mix_p[:, i:i+model.chunk_size])
mix_waves.append(waves)
i += gen_size
mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu)
with torch.no_grad():
_ort = self.onnx_models[mod]
spek = model.stft(mix_waves)
tar_waves = model.istft(torch.tensor(_ort.run(None, {'input': spek.cpu().numpy()})[0]))#.cpu()
tar_signal = tar_waves[:,:,trim:-trim].transpose(0,1).reshape(2, -1).numpy()[:, :-pad]
start = 0 if mix == 0 else margin_size
end = None if mix == list(mixes.keys())[::-1][0] else -margin_size
if margin_size == 0:
end = None
sources.append(tar_signal[:,start:end])
chunked_sources.append(sources)
_sources = np.concatenate(chunked_sources, axis=-1)
del self.onnx_models
if onnxitera == 1:
widget_text.write('Done!\n')
else:
widget_text.write('\n')
return _sources
def demix_demucs(self, mix, margin_size):
processed = {}
demucsitera = len(mix)
demucsitera_calc = demucsitera * 2
gui_progress_bar_demucs = 0
progress_bar = 0
if demucsitera == 1:
widget_text.write(base_text + f"Running Demucs Inference... ")
else:
widget_text.write(base_text + f"Running Demucs Inference...{space}\n")
print(' Running Demucs Inference...')
for nmix in mix:
gui_progress_bar_demucs += 1
progress_bar += 100
step = (progress_bar / demucsitera)
if demucsitera == 1:
pass
else:
percent_prog = f"{base_text}Demucs Inference Progress: {gui_progress_bar_demucs}/{demucsitera} | {round(step)}%"
widget_text.percentage(percent_prog)
update_progress(**progress_kwargs,
step=(0.35 + (1.05/demucsitera_calc * gui_progress_bar_demucs)))
cmix = mix[nmix]
cmix = torch.tensor(cmix, dtype=torch.float32)
ref = cmix.mean(0)
cmix = (cmix - ref.mean()) / ref.std()
with torch.no_grad():
sources = apply_model(self.demucs, cmix[None],
gui_progress_bar,
widget_text,
update_prog,
split=split_mode,
device=device,
overlap=overlap_set,
shifts=shift_set,
progress=False,
segmen=False,
**progress_demucs_kwargs)[0]
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
sources[[0,1]] = sources[[1,0]]
start = 0 if nmix == 0 else margin_size
end = None if nmix == list(mix.keys())[::-1][0] else -margin_size
if margin_size == 0:
end = None
processed[nmix] = sources[:,:,start:end].copy()
sources = list(processed.values())
sources = np.concatenate(sources, axis=-1)
if demucsitera == 1:
widget_text.write('Done!\n')
else:
widget_text.write('\n')
#print('the demucs model is done running')
return sources
def demix_demucs_split(self, mix):
if split_mode:
widget_text.write(base_text + f"Running Demucs Inference...{space}\n")
else:
widget_text.write(base_text + f"Running Demucs Inference... ")
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],
gui_progress_bar,
widget_text,
update_prog,
split=split_mode,
device=device,
overlap=overlap_set,
shifts=shift_set,
progress=False,
segmen=True,
**progress_demucs_kwargs)[0]
if split_mode:
widget_text.write('\n')
else:
widget_text.write('Done!\n')
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
sources[[0,1]] = sources[[1,0]]
return sources
def demix_demucs_v1(self, mix, margin_size):
processed = {}
demucsitera = len(mix)
demucsitera_calc = demucsitera * 2
gui_progress_bar_demucs = 0
progress_bar = 0
print(' Running Demucs Inference...')
if demucsitera == 1:
widget_text.write(base_text + f"Running Demucs v1 Inference... ")
else:
widget_text.write(base_text + f"Running Demucs v1 Inference...{space}\n")
for nmix in mix:
gui_progress_bar_demucs += 1
progress_bar += 100
step = (progress_bar / demucsitera)
if demucsitera == 1:
pass
else:
percent_prog = f"{base_text}Demucs v1 Inference Progress: {gui_progress_bar_demucs}/{demucsitera} | {round(step)}%"
widget_text.percentage(percent_prog)
update_progress(**progress_kwargs,
step=(0.35 + (1.05/demucsitera_calc * gui_progress_bar_demucs)))
cmix = mix[nmix]
cmix = torch.tensor(cmix, dtype=torch.float32)
ref = cmix.mean(0)
cmix = (cmix - ref.mean()) / ref.std()
with torch.no_grad():
sources = apply_model_v1(self.demucs,
cmix.to(device),
gui_progress_bar,
widget_text,
update_prog,
split=split_mode,
segmen=False,
shifts=shift_set,
**progress_demucs_kwargs)
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
sources[[0,1]] = sources[[1,0]]
start = 0 if nmix == 0 else margin_size
end = None if nmix == list(mix.keys())[::-1][0] else -margin_size
if margin_size == 0:
end = None
processed[nmix] = sources[:,:,start:end].copy()
sources = list(processed.values())
sources = np.concatenate(sources, axis=-1)
if demucsitera == 1:
widget_text.write('Done!\n')
else:
widget_text.write('\n')
return sources
def demix_demucs_v1_split(self, mix):
print(' Running Demucs Inference...')
if split_mode:
widget_text.write(base_text + f"Running Demucs v1 Inference...{space}\n")
else:
widget_text.write(base_text + f"Running Demucs v1 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_v1(self.demucs,
mix.to(device),
gui_progress_bar,
widget_text,
update_prog,
split=split_mode,
segmen=True,
shifts=shift_set,
**progress_demucs_kwargs)
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
sources[[0,1]] = sources[[1,0]]
if split_mode:
widget_text.write('\n')
else:
widget_text.write('Done!\n')
return sources
def demix_demucs_v2(self, mix, margin_size):
processed = {}
demucsitera = len(mix)
demucsitera_calc = demucsitera * 2
gui_progress_bar_demucs = 0
progress_bar = 0
if demucsitera == 1:
widget_text.write(base_text + f"Running Demucs v2 Inference... ")
else:
widget_text.write(base_text + f"Running Demucs v2 Inference...{space}\n")
for nmix in mix:
gui_progress_bar_demucs += 1
progress_bar += 100
step = (progress_bar / demucsitera)
if demucsitera == 1:
pass
else:
percent_prog = f"{base_text}Demucs v2 Inference Progress: {gui_progress_bar_demucs}/{demucsitera} | {round(step)}%"
widget_text.percentage(percent_prog)
update_progress(**progress_kwargs,
step=(0.35 + (1.05/demucsitera_calc * gui_progress_bar_demucs)))
cmix = mix[nmix]
cmix = torch.tensor(cmix, dtype=torch.float32)
ref = cmix.mean(0)
cmix = (cmix - ref.mean()) / ref.std()
with torch.no_grad():
sources = apply_model_v2(self.demucs,
cmix.to(device),
gui_progress_bar,
widget_text,
update_prog,
split=split_mode,
segmen=False,
overlap=overlap_set,
shifts=shift_set,
**progress_demucs_kwargs)
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
sources[[0,1]] = sources[[1,0]]
start = 0 if nmix == 0 else margin_size
end = None if nmix == list(mix.keys())[::-1][0] else -margin_size
if margin_size == 0:
end = None
processed[nmix] = sources[:,:,start:end].copy()
sources = list(processed.values())
sources = np.concatenate(sources, axis=-1)
if demucsitera == 1:
widget_text.write('Done!\n')
else:
widget_text.write('\n')
return sources
def demix_demucs_v2_split(self, mix):
print(' Running Demucs Inference...')
if split_mode:
widget_text.write(base_text + f"Running Demucs v2 Inference...{space}\n")
else:
widget_text.write(base_text + f"Running Demucs v2 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_v2(self.demucs,
mix.to(device),
gui_progress_bar,
widget_text,
update_prog,
split=split_mode,
segmen=True,
overlap=overlap_set,
shifts=shift_set,
**progress_demucs_kwargs)
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
sources[[0,1]] = sources[[1,0]]
if split_mode:
widget_text.write('\n')
else:
widget_text.write('Done!\n')
return sources
data = {
'autocompensate': True,
'aud_mdx': True,
'bit': '',
'chunks': 10,
'compensate': 1.03597672895,
'demucs_only': False,
'demucsmodel': False,
'DemucsModel_MDX': 'UVR_Demucs_Model_1',
'dim_f': 2048,
'export_path': None,
'flactype': 'PCM_16',
'gpu': -1,
'input_paths': None,
'inst_only': False,
'margin': 44100,
'mdxnetModel': 'UVR-MDX-NET Main',
'mdxnetModeltype': 'Vocals (Custom)',
'mixing': 'Default',
'modelFolder': False,
'mp3bit': '320k',
'n_fft_scale': 6144,
'no_chunk': False,
'noise_pro_select': 'Auto Select',
'noisereduc_s': 3,
'non_red': False,
'nophaseinst': True,
'normalize': False,
'overlap': 0.5,
'saveFormat': 'Wav',
'segment': 'Default',
'shifts': 0,
'split_mode': False,
'voc_only': False,
'wavtype': 'PCM_16',
}
default_chunks = data['chunks']
default_noisereduc_s = data['noisereduc_s']
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 += base * 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
warnings.filterwarnings("ignore")
cpu = torch.device('cpu')
def hide_opt():
with open(os.devnull, "w") as devnull:
old_stdout = sys.stdout
sys.stdout = devnull
try:
yield
finally:
sys.stdout = old_stdout
def main(window: tk.Wm,
text_widget: tk.Text,
button_widget: tk.Button,
progress_var: tk.Variable,
stop_thread,
**kwargs: dict):
global widget_text
global gui_progress_bar
global music_file
global default_chunks
global default_noisereduc_s
global _basename
global _mixture
global modeltype
global n_fft_scale_set
global dim_f_set
global progress_kwargs
global progress_demucs_kwargs
global base_text
global model_set_name
global mdx_model_name
global stemset_n
global stem_text_a
global stem_text_b
global noise_pro_set
global demucs_model_set
global autocompensate
global compensate
global channel_set
global margin_set
global overlap_set
global shift_set
global source_val
global split_mode
global demucs_model_set
global wav_type_set
global flac_type_set
global mp3_bit_set
global normalization_set
global demucs_model_version
global mdx_model_path
global widget_button
global stime
global model_hash
global demucs_switch
global no_chunk_demucs
global inst_only
global voc_only
global space
global main_window
global stop_button
stop_button = stop_thread
widget_text = text_widget
gui_progress_bar = progress_var
widget_button = button_widget
main_window = window
#Error Handling
onnxmissing = "[ONNXRuntimeError] : 3 : NO_SUCHFILE"
onnxmemerror = "onnxruntime::CudaCall CUDA failure 2: out of memory"
onnxmemerror2 = "onnxruntime::BFCArena::AllocateRawInternal"
systemmemerr = "DefaultCPUAllocator: not enough memory"
runtimeerr = "CUDNN error executing cudnnSetTensorNdDescriptor"
cuda_err = "CUDA out of memory"
mod_err = "ModuleNotFoundError"
file_err = "FileNotFoundError"
ffmp_err = """audioread\__init__.py", line 116, in audio_open"""
sf_write_err = "sf.write"
model_adv_set_err = "Got invalid dimensions for input"
demucs_model_missing_err = "is neither a single pre-trained model or a bag of models."
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' +
f'\nLast Conversion Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
data.update(kwargs)
global update_prog
# Update default settings
update_prog = update_progress
default_chunks = data['chunks']
default_noisereduc_s = data['noisereduc_s']
no_chunk_demucs = data['no_chunk']
space = ' '*90
if data['DemucsModel_MDX'] == "Tasnet v1":
demucs_model_set_name = 'tasnet.th'
demucs_model_version = 'v1'
elif data['DemucsModel_MDX'] == "Tasnet_extra v1":
demucs_model_set_name = 'tasnet_extra.th'
demucs_model_version = 'v1'
elif data['DemucsModel_MDX'] == "Demucs v1":
demucs_model_set_name = 'demucs.th'
demucs_model_version = 'v1'
elif data['DemucsModel_MDX'] == "Demucs v1.gz":
demucs_model_set_name = 'demucs.th.gz'
demucs_model_version = 'v1'
elif data['DemucsModel_MDX'] == "Demucs_extra v1":
demucs_model_set_name = 'demucs_extra.th'
demucs_model_version = 'v1'
elif data['DemucsModel_MDX'] == "Demucs_extra v1.gz":
demucs_model_set_name = 'demucs_extra.th.gz'
demucs_model_version = 'v1'
elif data['DemucsModel_MDX'] == "Light v1":
demucs_model_set_name = 'light.th'
demucs_model_version = 'v1'
elif data['DemucsModel_MDX'] == "Light v1.gz":
demucs_model_set_name = 'light.th.gz'
demucs_model_version = 'v1'
elif data['DemucsModel_MDX'] == "Light_extra v1":
demucs_model_set_name = 'light_extra.th'
demucs_model_version = 'v1'
elif data['DemucsModel_MDX'] == "Light_extra v1.gz":
demucs_model_set_name = 'light_extra.th.gz'
demucs_model_version = 'v1'
elif data['DemucsModel_MDX'] == "Tasnet v2":
demucs_model_set_name = 'tasnet-beb46fac.th'
demucs_model_version = 'v2'
elif data['DemucsModel_MDX'] == "Tasnet_extra v2":
demucs_model_set_name = 'tasnet_extra-df3777b2.th'
demucs_model_version = 'v2'
elif data['DemucsModel_MDX'] == "Demucs48_hq v2":
demucs_model_set_name = 'demucs48_hq-28a1282c.th'
demucs_model_version = 'v2'
elif data['DemucsModel_MDX'] == "Demucs v2":
demucs_model_set_name = 'demucs-e07c671f.th'
demucs_model_version = 'v2'
elif data['DemucsModel_MDX'] == "Demucs_extra v2":
demucs_model_set_name = 'demucs_extra-3646af93.th'
demucs_model_version = 'v2'
elif data['DemucsModel_MDX'] == "Demucs_unittest v2":
demucs_model_set_name = 'demucs_unittest-09ebc15f.th'
demucs_model_version = 'v2'
elif '.ckpt' in data['DemucsModel_MDX'] and 'v2' in data['DemucsModel_MDX']:
demucs_model_set_name = data['DemucsModel_MDX']
demucs_model_version = 'v2'
elif '.ckpt' in data['DemucsModel_MDX'] and 'v1' in data['DemucsModel_MDX']:
demucs_model_set_name = data['DemucsModel_MDX']
demucs_model_version = 'v1'
elif '.gz' in data['DemucsModel_MDX']:
demucs_model_set_name = data['DemucsModel_MDX']
demucs_model_version = 'v1'
else:
demucs_model_set_name = data['DemucsModel_MDX']
demucs_model_version = 'v3'
autocompensate = data['autocompensate']
model_set_name = data['mdxnetModel']
if model_set_name == 'UVR-MDX-NET 1':
mdx_model_name = 'UVR_MDXNET_1_9703'
elif model_set_name == 'UVR-MDX-NET 2':
mdx_model_name = 'UVR_MDXNET_2_9682'
elif model_set_name == 'UVR-MDX-NET 3':
mdx_model_name = 'UVR_MDXNET_3_9662'
elif model_set_name == 'UVR-MDX-NET Karaoke':
mdx_model_name = 'UVR_MDXNET_KARA'
elif model_set_name == 'UVR-MDX-NET Main':
mdx_model_name = 'UVR_MDXNET_Main'
elif model_set_name == 'UVR-MDX-NET Inst 1':
mdx_model_name = 'UVR_MDXNET_Inst_1'
elif model_set_name == 'UVR-MDX-NET Inst 2':
mdx_model_name = 'UVR_MDXNET_Inst_2'
else:
mdx_model_name = data['mdxnetModel']
mdx_model_path = f'models/MDX_Net_Models/{mdx_model_name}.onnx'
model_hash = hashlib.md5(open(mdx_model_path,'rb').read()).hexdigest()
model_params = []
model_params = lib_v5.filelist.provide_mdx_model_param_name(model_hash)
modeltype = model_params[0]
noise_pro = model_params[1]
stemset_n = model_params[2]
compensate_set = model_params[3]
source_val = model_params[4]
n_fft_scale_set = model_params[5]
dim_f_set = model_params[6]
if not data['aud_mdx']:
if data['mdxnetModeltype'] == 'Vocals (Custom)':
modeltype = 'v'
source_val = 3
stemset_n = '(Vocals)'
n_fft_scale_set = data['n_fft_scale']
dim_f_set = data['dim_f']
if data['mdxnetModeltype'] == 'Instrumental (Custom)':
modeltype = 'v'
source_val = 0
stemset_n = '(Instrumental)'
n_fft_scale_set = data['n_fft_scale']
dim_f_set = data['dim_f']
if data['mdxnetModeltype'] == 'Other (Custom)':
modeltype = 'v'
source_val = 2
stemset_n = '(Other)'
n_fft_scale_set = data['n_fft_scale']
dim_f_set = data['dim_f']
if data['mdxnetModeltype'] == 'Drums (Custom)':
modeltype = 'v'
source_val = 1
stemset_n = '(Drums)'
n_fft_scale_set = data['n_fft_scale']
dim_f_set = data['dim_f']
if data['mdxnetModeltype'] == 'Bass (Custom)':
modeltype = 'v'
source_val = 0
stemset_n = '(Bass)'
n_fft_scale_set = data['n_fft_scale']
dim_f_set = data['dim_f']
if stemset_n == '(Vocals)':
stem_text_a = 'Vocals'
stem_text_b = 'Instrumental'
elif stemset_n == '(Instrumental)':
stem_text_a = 'Instrumental'
stem_text_b = 'Vocals'
elif stemset_n == '(Other)':
stem_text_a = 'Other'
stem_text_b = 'the no \"Other\" track'
elif stemset_n == '(Drums)':
stem_text_a = 'Drums'
stem_text_b = 'the no \"Drums\" track'
elif stemset_n == '(Bass)':
stem_text_a = 'Bass'
stem_text_b = 'the no \"Bass\" track'
else:
stem_text_a = 'Vocals'
stem_text_b = 'Instrumental'
if autocompensate:
compensate = compensate_set
else:
compensate = data['compensate']
if data['noise_pro_select'] == 'Auto Select':
noise_pro_set = noise_pro
else:
noise_pro_set = data['noise_pro_select']
if data['wavtype'] == '32-bit Float':
wav_type_set = 'FLOAT'
elif data['wavtype'] == '64-bit Float':
wav_type_set = 'DOUBLE'
else:
wav_type_set = data['wavtype']
flac_type_set = data['flactype']
mp3_bit_set = data['mp3bit']
if data['normalize'] == True:
normalization_set = spec_utils.normalize
#print('normalization on')
else:
normalization_set = spec_utils.nonormalize
#print('normalization off')
#print(n_fft_scale_set)
#print(dim_f_set)
#print(demucs_model_set_name)
inst_only = data['inst_only']
voc_only = data['voc_only']
stime = time.perf_counter()
progress_var.set(0)
text_widget.clear()
button_widget.configure(state=tk.DISABLED) # Disable Button
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 = demucs_model_set_name
split_mode = data['split_mode']
demucs_switch = data['demucsmodel']
if data['wavtype'] == '64-bit Float':
if data['saveFormat'] == 'Flac':
text_widget.write('Please select \"WAV\" as your save format to use 64-bit Float.\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
if data['wavtype'] == '64-bit Float':
if data['saveFormat'] == 'Mp3':
text_widget.write('Please select \"WAV\" as your save format to use 64-bit Float.\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
_mixture = f'{data["input_paths"]}'
timestampnum = round(datetime.utcnow().timestamp())
randomnum = randrange(100000, 1000000)
if data['settest']:
try:
_basename = f'{data["export_path"]}/{str(timestampnum)}_{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
except:
_basename = f'{data["export_path"]}/{str(randomnum)}_{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
else:
_basename = f'{data["export_path"]}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
inference_type = 'inference_mdx'
# -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}
progress_demucs_kwargs = {'total_files': len(data['input_paths']),
'file_num': file_num, 'inference_type': inference_type}
if 'UVR' in demucs_model_set:
if stemset_n == '(Bass)' or stemset_n == '(Drums)' or stemset_n == '(Other)':
widget_text.write('The selected Demucs model can only be used with vocal or instrumental stems.\n')
widget_text.write('Please select a 4 stem Demucs model and try again.\n\n')
widget_text.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
gui_progress_bar.set(0)
widget_button.configure(state=tk.NORMAL) # Enable Button
return
if stemset_n == '(Instrumental)':
if not 'UVR' in demucs_model_set:
if data['demucsmodel']:
widget_text.write(base_text + 'The selected Demucs model cannot be used with this model.\n')
widget_text.write(base_text + 'Only 2 stem Demucs models are compatible with this model.\n')
widget_text.write(base_text + 'Setting Demucs model to \"UVR_Demucs_Model_1\".\n\n')
demucs_model_set = 'UVR_Demucs_Model_1'
try:
if float(data['noisereduc_s']) >= 11:
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')
text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n')
text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n')
text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n')
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if int(free/1.074e+9) in [3, 4, 5, 6, 7, 8]:
text_widget.write('Warning: Your main drive is running low on storage. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly.\n\n')
text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n')
text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n')
text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n\n')
except:
pass
if data['noisereduc_s'] == 'None':
pass
else:
if not os.path.isfile("lib_v5\sox\sox.exe"):
data['noisereduc_s'] = 'None'
data['non_red'] = False
widget_text.write(base_text + 'SoX is missing and required for noise reduction.\n')
widget_text.write(base_text + 'See the \"More Info\" tab in the Help Guide.\n')
widget_text.write(base_text + 'Noise Reduction will be disabled until SoX is available.\n\n')
update_progress(**progress_kwargs,
step=0)
e = os.path.join(data["export_path"])
demucsmodel = 'models/Demucs_Models/' + str(data['DemucsModel_MDX'])
pred = Predictor()
print('\n\nmodeltype: ', modeltype)
print('noise_pro: ', noise_pro)
print('stemset_n: ', stemset_n)
print('compensate_set: ', compensate_set)
print('source_val: ', source_val)
print('n_fft_scale_set: ', n_fft_scale_set)
print('dim_f_set: ', dim_f_set, '\n')
if modeltype == 'Not Set' or \
noise_pro == 'Not Set' or \
stemset_n == 'Not Set' or \
compensate_set == 'Not Set' or \
source_val == 'Not Set' or \
n_fft_scale_set == 'Not Set' or \
dim_f_set == 'Not Set':
confirm = tk.messagebox.askyesno(title='Unrecognized Model Detected',
message=f'\nWould you like to set the correct model parameters for this model before continuing?\n')
if confirm:
pred.mdx_options()
else:
text_widget.write(f'An unrecognized model has been detected.\n\n')
text_widget.write(f'Please configure the ONNX model settings accordingly and try again.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
pred.prediction_setup()
#print(demucsmodel)
# split
pred.prediction(
m=music_file,
)
except Exception as e:
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
message = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n'
if runtimeerr in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'Your PC cannot process this audio file with the chunk size selected.\nPlease lower the chunk size and try again.\n\n')
text_widget.write(f'If this error persists, please contact the developers.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: MDX-Net\n\n' +
f'Your PC cannot process this audio file with the chunk size selected.\nPlease lower the chunk size and try again.\n\n' +
f'If this error persists, please contact the developers.\n\n' +
f'Raw error details:\n\n' +
message + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if cuda_err in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n')
text_widget.write(f'Please close any GPU intensive applications and try again.\n')
text_widget.write(f'If the error persists, your GPU might not be supported.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: MDX-Net\n\n' +
f'The application was unable to allocate enough GPU memory to use this model.\n' +
f'Please close any GPU intensive applications and try again.\n' +
f'If the error persists, your GPU might not be supported.\n\n' +
f'Raw error details:\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if mod_err in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'Application files(s) are missing.\n')
text_widget.write("\n" + f'{type(e).__name__} - "{e}"' + "\n\n")
text_widget.write(f'Please check for missing files/scripts in the app directory and try again.\n')
text_widget.write(f'If the error persists, please reinstall application or contact the developers.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: MDX-Net\n\n' +
f'Application files(s) are missing.\n' +
f'Please check for missing files/scripts in the app directory and try again.\n' +
f'If the error persists, please reinstall application or contact the developers.\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if file_err in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'Missing file error raised.\n')
text_widget.write("\n" + f'{type(e).__name__} - "{e}"' + "\n\n")
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.\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:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: MDX-Net\n\n' +
f'Missing file error raised.\n' +
"\n" + f'Please address the error and try again.' + "\n" +
f'If this error persists, please contact the developers.\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if ffmp_err in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'The input file type is not supported or FFmpeg is missing.\n')
text_widget.write(f'Please select a file type supported by FFmpeg and try again.\n\n')
text_widget.write(f'If FFmpeg is missing or not installed, you will only be able to process \".wav\" files \nuntil it is available on this system.\n\n')
text_widget.write(f'See the \"More Info\" tab in the Help Guide.\n\n')
text_widget.write(f'If this error persists, please contact the developers.\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:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: MDX-Net\n\n' +
f'The input file type is not supported or FFmpeg is missing.\nPlease select a file type supported by FFmpeg and try again.\n\n' +
f'If FFmpeg is missing or not installed, you will only be able to process \".wav\" files until it is available on this system.\n\n' +
f'See the \"More Info\" tab in the Help Guide.\n\n' +
f'If this error persists, please contact the developers.\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if onnxmissing in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'The application could not detect this MDX-Net model on your system.\n')
text_widget.write(f'Please make sure all the models are present in the correct directory.\n')
text_widget.write(f'If the error persists, please reinstall application or contact the developers.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: MDX-Net\n\n' +
f'The application could not detect this MDX-Net model on your system.\n' +
f'Please make sure all the models are present in the correct directory.\n' +
f'If the error persists, please reinstall application or contact the developers.\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if onnxmemerror in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n')
text_widget.write(f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n')
text_widget.write(f'If the error persists, your GPU might not be supported.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: MDX-Net\n\n' +
f'The application was unable to allocate enough GPU memory to use this model.\n' +
f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n' +
f'If the error persists, your GPU might not be supported.\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if onnxmemerror2 in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n')
text_widget.write(f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n')
text_widget.write(f'If the error persists, your GPU might not be supported.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: MDX-Net\n\n' +
f'The application was unable to allocate enough GPU memory to use this model.\n' +
f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n' +
f'If the error persists, your GPU might not be supported.\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if sf_write_err in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'Could not write audio file.\n')
text_widget.write(f'This could be due to low storage on target device or a system permissions issue.\n')
text_widget.write(f"\nGo to the Settings Menu and click \"Open Error Log\" for raw error details.\n")
text_widget.write(f'\nIf the error persists, please contact the developers.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: MDX-Net\n\n' +
f'Could not write audio file.\n' +
f'This could be due to low storage on target device or a system permissions issue.\n' +
f'If the error persists, please contact the developers.\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if systemmemerr in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'The application was unable to allocate enough system memory to use this \nmodel.\n\n')
text_widget.write(f'Please do the following:\n\n1. Restart this application.\n2. Ensure any CPU intensive applications are closed.\n3. Then try again.\n\n')
text_widget.write(f'Please Note: Intel Pentium and Intel Celeron processors do not work well with \nthis application.\n\n')
text_widget.write(f'If the error persists, the system may not have enough RAM, or your CPU might \nnot be supported.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: MDX-Net\n\n' +
f'The application was unable to allocate enough system memory to use this model.\n' +
f'Please do the following:\n\n1. Restart this application.\n2. Ensure any CPU intensive applications are closed.\n3. Then try again.\n\n' +
f'Please Note: Intel Pentium and Intel Celeron processors do not work well with this application.\n\n' +
f'If the error persists, the system may not have enough RAM, or your CPU might \nnot be supported.\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if model_adv_set_err in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'The current ONNX model settings are not compatible with the selected \nmodel.\n\n')
text_widget.write(f'Please re-configure the advanced ONNX model settings accordingly and try \nagain.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: MDX-Net\n\n' +
f'The current ONNX model settings are not compatible with the selected model.\n\n' +
f'Please re-configure the advanced ONNX model settings accordingly and try again.\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if demucs_model_missing_err in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'The selected Demucs model is missing.\n\n')
text_widget.write(f'Please download the model or make sure it is in the correct directory.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: MDX-Net\n\n' +
f'The selected Demucs model is missing.\n\n' +
f'Please download the model or make sure it is in the correct directory.\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
print(traceback_text)
print(type(e).__name__, e)
print(message)
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: MDX-Net\n\n' +
f'If this error persists, please contact the developers with the error details.\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
tk.messagebox.showerror(master=window,
title='Error Details',
message=message)
progress_var.set(0)
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n')
text_widget.write("\nGo to the Settings Menu and click \"Open Error Log\" for raw error details.\n")
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)))}')
try:
torch.cuda.empty_cache()
except:
pass
button_widget.configure(state=tk.NORMAL) # Enable Button
return
progress_var.set(0)
text_widget.write(f'\nConversion(s) Completed!\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
if __name__ == '__main__':
start_time = time.time()
main()
print("Successfully completed music demixing.");print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
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