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inference_MDX.py
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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
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)
top= Toplevel()
top.geometry("740x550")
window_height = 740
window_width = 550
top.title("Specify Parameters")
top.resizable(False, False) # This code helps to disable windows from resizing
top.attributes("-topmost", True)
screen_width = top.winfo_screenwidth()
screen_height = top.winfo_screenheight()
x_cordinate = int((screen_width/2) - (window_width/2))
y_cordinate = int((screen_height/2) - (window_height/2))
top.geometry("{}x{}+{}+{}".format(window_width, window_height, x_cordinate, y_cordinate))
# change title bar icon
top.iconbitmap('img\\UVR-Icon-v2.ico')
tabControl = ttk.Notebook(top)
tabControl.pack(expand = 1, fill ="both")
tabControl.grid_rowconfigure(0, weight=1)
tabControl.grid_columnconfigure(0, weight=1)
frame0=Frame(tabControl,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'{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
top.destroy()
return
l0=ttk.Button(frame0,text="Stop Process", command=stop)
l0.grid(row=13,column=1,padx=0,pady=30)
def change_event():
self.okVar.set(1)
#top.destroy()
pass
top.protocol("WM_DELETE_WINDOW", change_event)
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 = 'the no \"Other\" track'
elif stemset_n == '(Drums)':
stem_text_a = 'Drums'
stem_text_b = 'no \"Drums\" track'
elif stemset_n == '(Bass)':
stem_text_a = 'Bass'
stem_text_b = 'No \"Bass\" track'
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'
top.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 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')
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")
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}_{model_set_name}',)
vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{vocal_name}_{model_set_name}',)
vocal_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{vocal_name}_{model_set_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}_{model_set_name}',)
Instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{model_set_name}',)
Instrumental_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{model_set_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}_{model_set_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}_{model_set_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}_{model_set_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}_{model_set_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}_{model_set_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}_{model_set_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):
# 1 = demucs only
# 0 = onnx only
if data['chunks'] == 'Full':
chunk_set = 0
else:
chunk_set = data['chunks']
if 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')
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'] == 'Full':
chunk_set = 0
widget_text.write(base_text + "Chunk size 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 split_mode == True:
sources = self.demix_demucs_split(mix)
if split_mode == False:
sources = self.demix_demucs(segmented_mix, margin_size=margin)
else: # both, apply spec effects
base_out = self.demix_base(segmented_mix, margin_size=margin)
#print(split_mode)