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))