from collections import defaultdict 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 functools import total_ordering from lib_v5 import dataset 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 statistics import mode from tqdm import tqdm from tqdm import tqdm from tkinter import filedialog 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 lib_v5.filelist import cv2 import gzip import hashlib import importlib import librosa import json import math import numpy as np import numpy as np import onnxruntime as ort import os import pathlib import psutil import pydub import re import shutil import soundfile as sf import soundfile as sf import subprocess import sys import time import time # Timer import tkinter as tk import torch import torch import traceback # Error Message Recent Calls import warnings 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 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') 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 & Set These Parameters", 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) #print('print from top ', model_hash) #source_val = 0 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 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 = 2 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 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 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 demucs_switch == 'on': #print('check model here: ', demucs_model_set) #'demucs.th.gz', 'demucs_extra.th.gz', 'light.th.gz', 'light_extra.th.gz' if 'tasnet.th' in demucs_model_set or 'tasnet_extra.th' in demucs_model_set or \ 'demucs.th' in demucs_model_set or \ 'demucs_extra.th' in demucs_model_set or 'light.th' in demucs_model_set or \ 'light_extra.th' in demucs_model_set or 'v1' in demucs_model_set or '.gz' in demucs_model_set: 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') elif 'tasnet-beb46fac.th' in demucs_model_set or 'tasnet_extra-df3777b2.th' in demucs_model_set or \ 'demucs48_hq-28a1282c.th' in demucs_model_set or'demucs-e07c671f.th' in demucs_model_set or \ 'demucs_extra-3646af93.th' in demucs_model_set or 'demucs_unittest-09ebc15f.th' in demucs_model_set or \ 'v2' in demucs_model_set: 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() else: 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 if demucs_only == 'on': pass else: self.models = get_models('tdf_extra', load=False, device=cpu, stems=modeltype, n_fft_scale=n_fft_scale_set, dim_f=dim_f_set) 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'] if demucs_only == 'off': self.onnx_models[c] = ort.InferenceSession(os.path.join('models/MDX_Net_Models', model_set), providers=run_type) #print(demucs_model_set) widget_text.write('Done!\n') elif demucs_only == 'on': #print(demucs_model_set) pass def prediction(self, m): mix, samplerate = librosa.load(m, mono=False, sr=44100) 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 inst_only = data['inst_only'] voc_only = data['voc_only'] 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 #Main Save Path save_path = os.path.dirname(base_name) #Write name if stemset_n == '(Vocals)': stem_text_a = 'Vocals' stem_text_b = 'Instrumental' elif stemset_n == '(Instrumental)': stem_text_a = 'Instrumental' stem_text_b = 'Vocals' #Vocal Path if stemset_n == '(Vocals)': vocal_name = '(Vocals)' elif stemset_n == '(Instrumental)': vocal_name = '(Instrumental)' if data['modelFolder']: vocal_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(base_name)}_{ModelName_2}_{vocal_name}',) else: vocal_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(base_name)}_{ModelName_2}_{vocal_name}',) #Instrumental Path if stemset_n == '(Vocals)': Instrumental_name = '(Instrumental)' elif stemset_n == '(Instrumental)': Instrumental_name = '(Vocals)' if data['modelFolder']: Instrumental_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(base_name)}_{ModelName_2}_{Instrumental_name}',) else: Instrumental_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(base_name)}_{ModelName_2}_{Instrumental_name}',) #Non-Reduced Vocal Path if stemset_n == '(Vocals)': vocal_name = '(Vocals)' 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(base_name)}_{ModelName_2}_{vocal_name}_No_Reduction',) else: non_reduced_vocal_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(base_name)}_{ModelName_2}_{vocal_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 = 'there' else: file_exists = 'not_there' if demucs_only == 'on': data['noisereduc_s'] == 'None' if not data['noisereduc_s'] == 'None': c += 1 if demucs_switch == 'off': if inst_only and not voc_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 and not voc_only: widget_text.write(base_text + f'Preparing to save {stem_text_b}...') else: widget_text.write(base_text + f'Saving {stem_text_a}... ') if demucs_only == 'on': if 'UVR' in model_set_name: sf.write(vocal_path, sources[1].T, samplerate, subtype=wav_type_set) update_progress(**progress_kwargs, step=(0.95)) widget_text.write('Done!\n') if 'extra' in model_set_name: sf.write(vocal_path, sources[3].T, samplerate, subtype=wav_type_set) update_progress(**progress_kwargs, step=(0.95)) widget_text.write('Done!\n') else: sf.write(non_reduced_vocal_path, sources[3].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 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 demucs_switch == 'off': 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: widget_text.write(base_text + f'Saving {stem_text_a}... ') if demucs_only == 'on': if 'UVR' in model_set_name: sf.write(vocal_path, sources[1].T, samplerate, subtype=wav_type_set) if 'extra' in model_set_name: sf.write(vocal_path, sources[3].T, samplerate, subtype=wav_type_set) else: sf.write(vocal_path, sources[3].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: 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=(0.95)) 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 == 'there': pass else: try: os.remove(vocal_path) except: pass widget_text.write('Done!\n') if data['noisereduc_s'] == 'None': 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) except: pass widget_text.write(base_text + 'Completed Separation!\n\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 demucs_switch == 'off': sources = self.demix_base(segmented_mix, margin_size=margin) elif demucs_only == 'on': if 'tasnet.th' in demucs_model_set or 'tasnet_extra.th' in demucs_model_set or \ 'demucs.th' in demucs_model_set or \ 'demucs_extra.th' in demucs_model_set or 'light.th' in demucs_model_set or \ 'light_extra.th' in demucs_model_set or 'v1' in demucs_model_set or '.gz' in demucs_model_set: sources = self.demix_demucs_v1(segmented_mix, margin_size=margin) elif 'tasnet-beb46fac.th' in demucs_model_set or 'tasnet_extra-df3777b2.th' in demucs_model_set or \ 'demucs48_hq-28a1282c.th' in demucs_model_set or'demucs-e07c671f.th' in demucs_model_set or \ 'demucs_extra-3646af93.th' in demucs_model_set or 'demucs_unittest-09ebc15f.th' in demucs_model_set or \ 'v2' in demucs_model_set: sources = self.demix_demucs_v2(segmented_mix, margin_size=margin) else: 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) if 'tasnet.th' in demucs_model_set or 'tasnet_extra.th' in demucs_model_set or \ 'demucs.th' in demucs_model_set or \ 'demucs_extra.th' in demucs_model_set or 'light.th' in demucs_model_set or \ 'light_extra.th' in demucs_model_set or 'v1' in demucs_model_set or '.gz' in demucs_model_set: demucs_out = self.demix_demucs_v1(segmented_mix, margin_size=margin) elif 'tasnet-beb46fac.th' in demucs_model_set or 'tasnet_extra-df3777b2.th' in demucs_model_set or \ 'demucs48_hq-28a1282c.th' in demucs_model_set or'demucs-e07c671f.th' in demucs_model_set or \ 'demucs_extra-3646af93.th' in demucs_model_set or 'demucs_unittest-09ebc15f.th' in demucs_model_set or \ 'v2' in demucs_model_set: demucs_out = self.demix_demucs_v2(segmented_mix, margin_size=margin) else: if split_mode == True: demucs_out = self.demix_demucs_split(mix) if split_mode == False: 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 = {} if 'UVR' in demucs_model_set: if stemset_n == '(Instrumental)': sources[3] = (spec_effects(wave=[demucs_out[0],base_out[0]], algorithm=data['mixing'], value=b[3])*float(compensate)) # compensation else: sources[3] = (spec_effects(wave=[demucs_out[1],base_out[0]], algorithm=data['mixing'], value=b[3])*float(compensate)) # compensation else: sources[3] = (spec_effects(wave=[demucs_out[3],base_out[0]], algorithm=data['mixing'], value=b[3])*float(compensate)) # compensation if demucs_switch == 'off': 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 widget_text.write(base_text + "Running ONNX Inference...\n") widget_text.write(base_text + "Processing "f"{onnxitera} slices... ") print(' Running ONNX Inference...') for mix in mixes: gui_progress_bar_onnx += 1 if demucs_switch == 'on': 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))) 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 widget_text.write('Done!\n') return _sources def demix_demucs(self, mix, margin_size): #print('shift_set ', shift_set) processed = {} demucsitera = len(mix) demucsitera_calc = demucsitera * 2 gui_progress_bar_demucs = 0 widget_text.write(base_text + "Split Mode is off. (Chunks enabled for Demucs Model)\n") widget_text.write(base_text + "Running Demucs Inference...\n") widget_text.write(base_text + "Processing "f"{len(mix)} slices... ") print('Running Demucs Inference...') for nmix in mix: gui_progress_bar_demucs += 1 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], split=split_mode, device=device, overlap=overlap_set, shifts=shift_set, progress=False)[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) widget_text.write('Done!\n') return sources def demix_demucs_split(self, mix): #print('shift_set ', shift_set) widget_text.write(base_text + "Split Mode is on. (Chunks disabled for Demucs Model)\n") widget_text.write(base_text + "Running Demucs Inference...\n") widget_text.write(base_text + "Processing "f"{len(mix)} slices... ") 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], split=split_mode, device=device, overlap=overlap_set, shifts=shift_set, progress=False)[0] widget_text.write('Done!\n') sources = (sources * ref.std() + ref.mean()).cpu().numpy() sources[[0,1]] = sources[[1,0]] return sources def demix_demucs_v2(self, mix, margin_size): processed = {} demucsitera = len(mix) demucsitera_calc = demucsitera * 2 gui_progress_bar_demucs = 0 widget_text.write(base_text + "Running Demucs v2 Inference...\n") widget_text.write(base_text + "Processing "f"{len(mix)} slices... ") print(' Running Demucs Inference...') for nmix in mix: gui_progress_bar_demucs += 1 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), split=split_mode, overlap=overlap_set, shifts=shift_set) 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) widget_text.write('Done!\n') return sources def demix_demucs_v1(self, mix, margin_size): processed = {} demucsitera = len(mix) demucsitera_calc = demucsitera * 2 gui_progress_bar_demucs = 0 widget_text.write(base_text + "Running Demucs v1 Inference...\n") widget_text.write(base_text + "Processing "f"{len(mix)} slices... ") print(' Running Demucs Inference...') for nmix in mix: gui_progress_bar_demucs += 1 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), split=split_mode, shifts=shift_set) 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) widget_text.write('Done!\n') return sources 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') device = torch.device('cuda:0' if torch.cuda.is_available() else '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 class VocalRemover(object): def __init__(self, data, text_widget: tk.Text): self.data = data self.text_widget = text_widget self.models = defaultdict(lambda: None) self.devices = defaultdict(lambda: None) # self.offset = model.offset 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 def determineModelFolderName(): """ Determine the name that is used for the folder and appended to the back of the music files """ modelFolderName = '' if not data['modelFolder']: # Model Test Mode not selected return modelFolderName # -Instrumental- if os.path.isfile(data['instrumentalModel']): modelFolderName += os.path.splitext(os.path.basename(data['instrumentalModel']))[0] if modelFolderName: modelFolderName = '/' + modelFolderName return modelFolderName class VocalRemover(object): def __init__(self, data, text_widget: tk.Text): self.data = data self.text_widget = text_widget # self.offset = model.offset data = { 'agg': 10, 'algo': 'Instrumentals (Min Spec)', 'appendensem': False, 'autocompensate': True, 'chunks': 'auto', 'compensate': 1.03597672895, 'demucs_only': False, 'demucsmodel': False, 'DemucsModel_MDX': 'UVR_Demucs_Model_1', 'ensChoose': 'Basic VR Ensemble', 'export_path': None, 'gpu': -1, 'high_end_process': 'mirroring', 'input_paths': None, 'inst_only': False, 'instrumentalModel': None, 'margin': 44100, 'mdx_ensem': 'MDX-Net: UVR-MDX-NET 1', 'mdx_ensem_b': 'No Model', 'mdx_only_ensem_a': 'MDX-Net: UVR-MDX-NET Main', 'mdx_only_ensem_b': 'MDX-Net: UVR-MDX-NET 1', 'mdx_only_ensem_c': 'No Model', 'mdx_only_ensem_d': 'No Model', 'mdx_only_ensem_e': 'No Model', 'mixing': 'Default', 'mp3bit': '320k', 'noise_pro_select': 'Auto Select', 'noisereduc_s': 3, 'non_red': False, 'normalize': False, 'output_image': True, 'overlap': 0.5, 'postprocess': True, 'saveFormat': 'wav', 'shifts': 0, 'split_mode': False, 'tta': True, 'useModel': None, 'voc_only': False, 'vr_ensem': '2_HP-UVR', 'vr_ensem_a': '1_HP-UVR', 'vr_ensem_b': '2_HP-UVR', 'vr_ensem_c': 'No Model', 'vr_ensem_d': 'No Model', 'vr_ensem_e': 'No Model', 'vr_ensem_mdx_a': 'No Model', 'vr_ensem_mdx_b': 'No Model', 'vr_ensem_mdx_c': 'No Model', 'vr_multi_USER_model_param_1': 'Auto', 'vr_multi_USER_model_param_2': 'Auto', 'vr_multi_USER_model_param_3': 'Auto', 'vr_multi_USER_model_param_4': 'Auto', 'vr_basic_USER_model_param_1': 'Auto', 'vr_basic_USER_model_param_2': 'Auto', 'vr_basic_USER_model_param_3': 'Auto', 'vr_basic_USER_model_param_4': 'Auto', 'vr_basic_USER_model_param_5': 'Auto', 'wavtype': 'PCM_16', 'window_size': 512 } default_window_size = data['window_size'] default_agg = data['agg'] 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 def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress_var: tk.Variable, **kwargs: dict): global widget_text global gui_progress_bar global music_file global default_chunks global default_noisereduc_s global base_name global progress_kwargs global base_text global modeltype global model_set global model_set_name global ModelName_2 global compensate global autocompensate global demucs_model_set global channel_set global margin_set global overlap_set global shift_set global noise_pro_set global n_fft_scale_set global dim_f_set global split_mode global demucs_switch global demucs_only global wav_type_set global flac_type_set global mp3_bit_set global model_hash global stime global stemset_n global source_val global widget_button wav_type_set = data['wavtype'] # Update default settings default_chunks = data['chunks'] default_noisereduc_s = data['noisereduc_s'] autocompensate = data['autocompensate'] widget_text = text_widget gui_progress_bar = progress_var widget_button = button_widget #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" enex_err = "local variable \'enseExport\' referenced before assignment" mod_err = "ModuleNotFoundError" file_err = "FileNotFoundError" ffmp_err = """audioread\__init__.py", line 116, in audio_open""" sf_write_err = "sf.write" 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: Ensemble Mode' + f'\nLast Conversion Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass global nn_arch_sizes global nn_architecture nn_arch_sizes = [ 31191, # default 33966, 123821, 123812, 129605, 537238, 537227 # custom ] def save_files(wav_instrument, wav_vocals): """Save output music files""" vocal_name = '(Vocals)' instrumental_name = '(Instrumental)' save_path = os.path.dirname(base_name) # Swap names if vocal model VModel="Vocal" if VModel in model_name: # Reverse names vocal_name, instrumental_name = instrumental_name, vocal_name # Save Temp File # For instrumental the instrumental is the temp file # and for vocal the instrumental is the temp file due # to reversement sf.write(f'temp.wav', normalization_set(wav_instrument), mp.param['sr'], subtype=wav_type_set) # -Save files- # Instrumental if instrumental_name is not None: instrumental_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(base_name)}_{ModelName_1}_{instrumental_name}', ) if VModel in ModelName_1 and data['voc_only']: sf.write(instrumental_path, normalization_set(wav_instrument), mp.param['sr'], subtype=wav_type_set) elif VModel in ModelName_1 and data['inst_only']: pass elif data['voc_only']: pass else: sf.write(instrumental_path, normalization_set(wav_instrument), mp.param['sr'], subtype=wav_type_set) # Vocal if vocal_name is not None: vocal_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name=f'{os.path.basename(base_name)}_{ModelName_1}_{vocal_name}', ) if VModel in ModelName_1 and data['inst_only']: sf.write(vocal_path, normalization_set(wav_vocals), mp.param['sr'], subtype=wav_type_set) elif VModel in ModelName_1 and data['voc_only']: pass elif data['inst_only']: pass else: sf.write(vocal_path, normalization_set(wav_vocals), mp.param['sr'], subtype=wav_type_set) data.update(kwargs) if data['DemucsModel_MDX'] == "Tasnet v1": demucs_model_set_name = 'tasnet.th' elif data['DemucsModel_MDX'] == "Tasnet_extra v1": demucs_model_set_name = 'tasnet_extra.th' elif data['DemucsModel_MDX'] == "Demucs v1": demucs_model_set_name = 'demucs.th' elif data['DemucsModel_MDX'] == "Demucs v1.gz": demucs_model_set_name = 'demucs.th.gz' elif data['DemucsModel_MDX'] == "Demucs_extra v1": demucs_model_set_name = 'demucs_extra.th' elif data['DemucsModel_MDX'] == "Demucs_extra v1.gz": demucs_model_set_name = 'demucs_extra.th.gz' elif data['DemucsModel_MDX'] == "Light v1": demucs_model_set_name = 'light.th' elif data['DemucsModel_MDX'] == "Light v1.gz": demucs_model_set_name = 'light.th.gz' elif data['DemucsModel_MDX'] == "Light_extra v1": demucs_model_set_name = 'light_extra.th' elif data['DemucsModel_MDX'] == "Light_extra v1.gz": demucs_model_set_name = 'light_extra.th.gz' elif data['DemucsModel_MDX'] == "Tasnet v2": demucs_model_set_name = 'tasnet-beb46fac.th' elif data['DemucsModel_MDX'] == "Tasnet_extra v2": demucs_model_set_name = 'tasnet_extra-df3777b2.th' elif data['DemucsModel_MDX'] == "Demucs48_hq v2": demucs_model_set_name = 'demucs48_hq-28a1282c.th' elif data['DemucsModel_MDX'] == "Demucs v2": demucs_model_set_name = 'demucs-e07c671f.th' elif data['DemucsModel_MDX'] == "Demucs_extra v2": demucs_model_set_name = 'demucs_extra-3646af93.th' elif data['DemucsModel_MDX'] == "Demucs_unittest v2": demucs_model_set_name = 'demucs_unittest-09ebc15f.th' elif '.ckpt' in data['DemucsModel_MDX'] and 'v2' in data['DemucsModel_MDX']: demucs_model_set_name = data['DemucsModel_MDX'] elif '.ckpt' in data['DemucsModel_MDX'] and 'v1' in data['DemucsModel_MDX']: demucs_model_set_name = data['DemucsModel_MDX'] else: demucs_model_set_name = data['DemucsModel_MDX'] if data['mdx_ensem'] == "Demucs: Tasnet v1": demucs_model_set_name_muilti_a = 'tasnet.th' elif data['mdx_ensem'] == "Demucs: Tasnet_extra v1": demucs_model_set_name_muilti_a = 'tasnet_extra.th' elif data['mdx_ensem'] == "Demucs: Demucs v1": demucs_model_set_name_muilti_a = 'demucs.th' elif data['mdx_ensem'] == "Demucs: Demucs_extra v1": demucs_model_set_name_muilti_a = 'demucs_extra.th' elif data['mdx_ensem'] == "Demucs: Light v1": demucs_model_set_name_muilti_a = 'light.th' elif data['mdx_ensem'] == "Demucs: Light_extra v1": demucs_model_set_name_muilti_a = 'light_extra.th' elif data['mdx_ensem'] == "Demucs: Demucs v1.gz": demucs_model_set_name_muilti_a = 'demucs.th.gz' elif data['mdx_ensem'] == "Demucs: Demucs_extra v1.gz": demucs_model_set_name_muilti_a = 'demucs_extra.th.gz' elif data['mdx_ensem'] == "Demucs: Light v1.gz": demucs_model_set_name_muilti_a = 'light.th.gz' elif data['mdx_ensem'] == "Demucs: Light_extra v1.gz": demucs_model_set_name_muilti_a = 'light_extra.th.gz' elif data['mdx_ensem'] == "Demucs: Tasnet v2": demucs_model_set_name_muilti_a = 'tasnet-beb46fac.th' elif data['mdx_ensem'] == "Demucs: Tasnet_extra v2": demucs_model_set_name_muilti_a = 'tasnet_extra-df3777b2.th' elif data['mdx_ensem'] == "Demucs: Demucs48_hq v2": demucs_model_set_name_muilti_a = 'demucs48_hq-28a1282c.th' elif data['mdx_ensem'] == "Demucs: Demucs v2": demucs_model_set_name_muilti_a = 'demucs-e07c671f.th' elif data['mdx_ensem'] == "Demucs: Demucs_extra v2": demucs_model_set_name_muilti_a = 'demucs_extra-3646af93.th' elif data['mdx_ensem'] == "Demucs: Demucs_unittest v2": demucs_model_set_name_muilti_a = 'demucs_unittest-09ebc15f.th' elif data['mdx_ensem'] == "Demucs: mdx_extra": demucs_model_set_name_muilti_a = 'mdx_extra' elif data['mdx_ensem'] == "Demucs: mdx_extra_q": demucs_model_set_name_muilti_a = 'mdx_extra_q' elif data['mdx_ensem'] == "Demucs: mdx": demucs_model_set_name_muilti_a = 'mdx' elif data['mdx_ensem'] == "Demucs: mdx_q": demucs_model_set_name_muilti_a = 'mdx_q' elif data['mdx_ensem'] == "Demucs: UVR_Demucs_Model_1": demucs_model_set_name_muilti_a = 'UVR_Demucs_Model_1' elif data['mdx_ensem'] == "Demucs: UVR_Demucs_Model_2": demucs_model_set_name_muilti_a = 'UVR_Demucs_Model_2' elif data['mdx_ensem'] == "Demucs: UVR_Demucs_Model_Bag": demucs_model_set_name_muilti_a = 'UVR_Demucs_Model_Bag' else: demucs_model_set_name_muilti_a = data['mdx_ensem'] if data['mdx_ensem_b'] == "Demucs: Tasnet v1": demucs_model_set_name_muilti_b = 'tasnet.th' elif data['mdx_ensem_b'] == "Demucs: Tasnet_extra v1": demucs_model_set_name_muilti_b = 'tasnet_extra.th' elif data['mdx_ensem_b'] == "Demucs: Demucs v1": demucs_model_set_name_muilti_b = 'demucs.th' elif data['mdx_ensem_b'] == "Demucs: Demucs_extra v1": demucs_model_set_name_muilti_b = 'demucs_extra.th' elif data['mdx_ensem_b'] == "Demucs: Light v1": demucs_model_set_name_muilti_b = 'light.th' elif data['mdx_ensem_b'] == "Demucs: Light_extra v1": demucs_model_set_name_muilti_b = 'light_extra.th' elif data['mdx_ensem_b'] == "Demucs: Demucs v1.gz": demucs_model_set_name_muilti_b = 'demucs.th.gz' elif data['mdx_ensem_b'] == "Demucs: Demucs_extra v1.gz": demucs_model_set_name_muilti_b = 'demucs_extra.th.gz' elif data['mdx_ensem_b'] == "Demucs: Light v1.gz": demucs_model_set_name_muilti_b = 'light.th.gz' elif data['mdx_ensem_b'] == "Demucs: Light_extra v1.gz": demucs_model_set_name_muilti_b = 'light_extra.th.gz' elif data['mdx_ensem_b'] == "Demucs: Tasnet v2": demucs_model_set_name_muilti_b = 'tasnet-beb46fac.th' elif data['mdx_ensem_b'] == "Demucs: Tasnet_extra v2": demucs_model_set_name_muilti_b = 'tasnet_extra-df3777b2.th' elif data['mdx_ensem_b'] == "Demucs: Demucs48_hq v2": demucs_model_set_name_muilti_b = 'demucs48_hq-28a1282c.th' elif data['mdx_ensem_b'] == "Demucs: Demucs v2": demucs_model_set_name_muilti_b = 'demucs-e07c671f.th' elif data['mdx_ensem_b'] == "Demucs: Demucs_extra v2": demucs_model_set_name_muilti_b = 'demucs_extra-3646af93.th' elif data['mdx_ensem_b'] == "Demucs: Demucs_unittest v2": demucs_model_set_name_muilti_b = 'demucs_unittest-09ebc15f.th' elif data['mdx_ensem_b'] == "Demucs: mdx_extra": demucs_model_set_name_muilti_b = 'mdx_extra' elif data['mdx_ensem_b'] == "Demucs: mdx_extra_q": demucs_model_set_name_muilti_b = 'mdx_extra_q' elif data['mdx_ensem_b'] == "Demucs: mdx": demucs_model_set_name_muilti_b = 'mdx' elif data['mdx_ensem_b'] == "Demucs: mdx_q": demucs_model_set_name_muilti_b = 'mdx_q' elif data['mdx_ensem_b'] == "Demucs: UVR_Demucs_Model_1": demucs_model_set_name_muilti_b = 'UVR_Demucs_Model_1' elif data['mdx_ensem_b'] == "Demucs: UVR_Demucs_Model_2": demucs_model_set_name_muilti_b = 'UVR_Demucs_Model_2' elif data['mdx_ensem_b'] == "Demucs: UVR_Demucs_Model_Bag": demucs_model_set_name_muilti_b = 'UVR_Demucs_Model_Bag' else: demucs_model_set_name_muilti_b = data['mdx_ensem_b'] if data['mdx_only_ensem_a'] == "Demucs: Tasnet v1": demucs_model_set_name_a = 'tasnet.th' elif data['mdx_only_ensem_a'] == "Demucs: Tasnet_extra v1": demucs_model_set_name_a = 'tasnet_extra.th' elif data['mdx_only_ensem_a'] == "Demucs: Demucs v1": demucs_model_set_name_a = 'demucs.th' elif data['mdx_only_ensem_a'] == "Demucs: Demucs_extra v1": demucs_model_set_name_a = 'demucs_extra.th' elif data['mdx_only_ensem_a'] == "Demucs: Light v1": demucs_model_set_name_a = 'light.th' elif data['mdx_only_ensem_a'] == "Demucs: Light_extra v1": demucs_model_set_name_a = 'light_extra.th' elif data['mdx_only_ensem_a'] == "Demucs: Demucs v1.gz": demucs_model_set_name_a = 'demucs.th.gz' elif data['mdx_only_ensem_a'] == "Demucs: Demucs_extra v1.gz": demucs_model_set_name_a = 'demucs_extra.th.gz' elif data['mdx_only_ensem_a'] == "Demucs: Light v1.gz": demucs_model_set_name_a = 'light.th.gz' elif data['mdx_only_ensem_a'] == "Demucs: Light_extra v1.gz": demucs_model_set_name_a = 'light_extra.th.gz' elif data['mdx_only_ensem_a'] == "Demucs: Tasnet v2": demucs_model_set_name_a = 'tasnet-beb46fac.th' elif data['mdx_only_ensem_a'] == "Demucs: Tasnet_extra v2": demucs_model_set_name_a = 'tasnet_extra-df3777b2.th' elif data['mdx_only_ensem_a'] == "Demucs: Demucs48_hq v2": demucs_model_set_name_a = 'demucs48_hq-28a1282c.th' elif data['mdx_only_ensem_a'] == "Demucs: Demucs v2": demucs_model_set_name_a = 'demucs-e07c671f.th' elif data['mdx_only_ensem_a'] == "Demucs: Demucs_extra v2": demucs_model_set_name_a = 'demucs_extra-3646af93.th' elif data['mdx_only_ensem_a'] == "Demucs: Demucs_unittest v2": demucs_model_set_name_a = 'demucs_unittest-09ebc15f.th' elif data['mdx_only_ensem_a'] == "Demucs: mdx_extra": demucs_model_set_name_a = 'mdx_extra' elif data['mdx_only_ensem_a'] == "Demucs: mdx_extra_q": demucs_model_set_name_a = 'mdx_extra_q' elif data['mdx_only_ensem_a'] == "Demucs: mdx": demucs_model_set_name_a = 'mdx' elif data['mdx_only_ensem_a'] == "Demucs: mdx_q": demucs_model_set_name_a = 'mdx_q' elif data['mdx_only_ensem_a'] == "Demucs: UVR_Demucs_Model_1": demucs_model_set_name_a = 'UVR_Demucs_Model_1' elif data['mdx_only_ensem_a'] == "Demucs: UVR_Demucs_Model_2": demucs_model_set_name_a = 'UVR_Demucs_Model_2' elif data['mdx_only_ensem_a'] == "Demucs: UVR_Demucs_Model_Bag": demucs_model_set_name_a = 'UVR_Demucs_Model_Bag' else: demucs_model_set_name_a = data['mdx_only_ensem_a'] if data['mdx_only_ensem_b'] == "Demucs: Tasnet v1": demucs_model_set_name_b = 'tasnet.th' elif data['mdx_only_ensem_b'] == "Demucs: Tasnet_extra v1": demucs_model_set_name_b = 'tasnet_extra.th' elif data['mdx_only_ensem_b'] == "Demucs: Demucs v1": demucs_model_set_name_b = 'demucs.th' elif data['mdx_only_ensem_b'] == "Demucs: Demucs_extra v1": demucs_model_set_name_b = 'demucs_extra.th' elif data['mdx_only_ensem_b'] == "Demucs: Light v1": demucs_model_set_name_b = 'light.th' elif data['mdx_only_ensem_b'] == "Demucs: Light_extra v1": demucs_model_set_name_b = 'light_extra.th' elif data['mdx_only_ensem_b'] == "Demucs: Demucs v1.gz": demucs_model_set_name_b = 'demucs.th.gz' elif data['mdx_only_ensem_b'] == "Demucs: Demucs_extra v1.gz": demucs_model_set_name_b = 'demucs_extra.th.gz' elif data['mdx_only_ensem_b'] == "Demucs: Light v1.gz": demucs_model_set_name_b = 'light.th.gz' elif data['mdx_only_ensem_b'] == "Demucs: Light_extra v1.gz": demucs_model_set_name_b = 'light_extra.th.gz' elif data['mdx_only_ensem_b'] == "Demucs: Tasnet v2": demucs_model_set_name_b = 'tasnet-beb46fac.th' elif data['mdx_only_ensem_b'] == "Demucs: Tasnet_extra v2": demucs_model_set_name_b = 'tasnet_extra-df3777b2.th' elif data['mdx_only_ensem_b'] == "Demucs: Demucs48_hq v2": demucs_model_set_name_b = 'demucs48_hq-28a1282c.th' elif data['mdx_only_ensem_b'] == "Demucs: Demucs v2": demucs_model_set_name_b = 'demucs-e07c671f.th' elif data['mdx_only_ensem_b'] == "Demucs: Demucs_extra v2": demucs_model_set_name_b = 'demucs_extra-3646af93.th' elif data['mdx_only_ensem_b'] == "Demucs: Demucs_unittest v2": demucs_model_set_name_b = 'demucs_unittest-09ebc15f.th' elif data['mdx_only_ensem_b'] == "Demucs: mdx_extra": demucs_model_set_name_b = 'mdx_extra' elif data['mdx_only_ensem_b'] == "Demucs: mdx_extra_q": demucs_model_set_name_b = 'mdx_extra_q' elif data['mdx_only_ensem_b'] == "Demucs: mdx": demucs_model_set_name_b = 'mdx' elif data['mdx_only_ensem_b'] == "Demucs: mdx_q": demucs_model_set_name_b = 'mdx_q' elif data['mdx_only_ensem_b'] == "Demucs: UVR_Demucs_Model_1": demucs_model_set_name_b = 'UVR_Demucs_Model_1' elif data['mdx_only_ensem_b'] == "Demucs: UVR_Demucs_Model_2": demucs_model_set_name_b = 'UVR_Demucs_Model_2' elif data['mdx_only_ensem_b'] == "Demucs: UVR_Demucs_Model_Bag": demucs_model_set_name_b = 'UVR_Demucs_Model_Bag' else: demucs_model_set_name_b = data['mdx_only_ensem_b'] if data['mdx_only_ensem_c'] == "Demucs: Tasnet v1": demucs_model_set_name_c = 'tasnet.th' elif data['mdx_only_ensem_c'] == "Demucs: Tasnet_extra v1": demucs_model_set_name_c = 'tasnet_extra.th' elif data['mdx_only_ensem_c'] == "Demucs: Demucs v1": demucs_model_set_name_c = 'demucs.th' elif data['mdx_only_ensem_c'] == "Demucs: Demucs_extra v1": demucs_model_set_name_c = 'demucs_extra.th' elif data['mdx_only_ensem_c'] == "Demucs: Light v1": demucs_model_set_name_c = 'light.th' elif data['mdx_only_ensem_c'] == "Demucs: Light_extra v1": demucs_model_set_name_c = 'light_extra.th' elif data['mdx_only_ensem_c'] == "Demucs: Demucs v1.gz": demucs_model_set_name_c = 'demucs.th.gz' elif data['mdx_only_ensem_c'] == "Demucs: Demucs_extra v1.gz": demucs_model_set_name_c = 'demucs_extra.th.gz' elif data['mdx_only_ensem_c'] == "Demucs: Light v1.gz": demucs_model_set_name_c = 'light.th.gz' elif data['mdx_only_ensem_c'] == "Demucs: Light_extra v1.gz": demucs_model_set_name_c = 'light_extra.th.gz' elif data['mdx_only_ensem_c'] == "Demucs: Tasnet v2": demucs_model_set_name_c = 'tasnet-beb46fac.th' elif data['mdx_only_ensem_c'] == "Demucs: Tasnet_extra v2": demucs_model_set_name_c = 'tasnet_extra-df3777b2.th' elif data['mdx_only_ensem_c'] == "Demucs: Demucs48_hq v2": demucs_model_set_name_c = 'demucs48_hq-28a1282c.th' elif data['mdx_only_ensem_c'] == "Demucs: Demucs v2": demucs_model_set_name_c = 'demucs-e07c671f.th' elif data['mdx_only_ensem_c'] == "Demucs: Demucs_extra v2": demucs_model_set_name_c = 'demucs_extra-3646af93.th' elif data['mdx_only_ensem_c'] == "Demucs: Demucs_unittest v2": demucs_model_set_name_c = 'demucs_unittest-09ebc15f.th' elif data['mdx_only_ensem_c'] == "Demucs: mdx_extra": demucs_model_set_name_c = 'mdx_extra' elif data['mdx_only_ensem_c'] == "Demucs: mdx_extra_q": demucs_model_set_name_c = 'mdx_extra_q' elif data['mdx_only_ensem_c'] == "Demucs: mdx": demucs_model_set_name_c = 'mdx' elif data['mdx_only_ensem_c'] == "Demucs: mdx_q": demucs_model_set_name_c = 'mdx_q' elif data['mdx_only_ensem_c'] == "Demucs: UVR_Demucs_Model_1": demucs_model_set_name_c = 'UVR_Demucs_Model_1' elif data['mdx_only_ensem_c'] == "Demucs: UVR_Demucs_Model_2": demucs_model_set_name_c = 'UVR_Demucs_Model_2' elif data['mdx_only_ensem_c'] == "Demucs: UVR_Demucs_Model_Bag": demucs_model_set_name_c = 'UVR_Demucs_Model_Bag' else: demucs_model_set_name_c = data['mdx_only_ensem_c'] if data['mdx_only_ensem_d'] == "Demucs: Tasnet v1": demucs_model_set_name_d = 'tasnet.th' elif data['mdx_only_ensem_d'] == "Demucs: Tasnet_extra v1": demucs_model_set_name_d = 'tasnet_extra.th' elif data['mdx_only_ensem_d'] == "Demucs: Demucs v1": demucs_model_set_name_d = 'demucs.th' elif data['mdx_only_ensem_d'] == "Demucs: Demucs_extra v1": demucs_model_set_name_d = 'demucs_extra.th' elif data['mdx_only_ensem_d'] == "Demucs: Light v1": demucs_model_set_name_d = 'light.th' elif data['mdx_only_ensem_d'] == "Demucs: Light_extra v1": demucs_model_set_name_d = 'light_extra.th' elif data['mdx_only_ensem_d'] == "Demucs: Demucs v1.gz": demucs_model_set_name_d = 'demucs.th.gz' elif data['mdx_only_ensem_d'] == "Demucs: Demucs_extra v1.gz": demucs_model_set_name_d = 'demucs_extra.th.gz' elif data['mdx_only_ensem_d'] == "Demucs: Light v1.gz": demucs_model_set_name_d = 'light.th.gz' elif data['mdx_only_ensem_d'] == "Demucs: Light_extra v1.gz": demucs_model_set_name_d = 'light_extra.th.gz' elif data['mdx_only_ensem_d'] == "Demucs: Tasnet v2": demucs_model_set_name_d = 'tasnet-beb46fac.th' elif data['mdx_only_ensem_d'] == "Demucs: Tasnet_extra v2": demucs_model_set_name_d = 'tasnet_extra-df3777b2.th' elif data['mdx_only_ensem_d'] == "Demucs: Demucs48_hq v2": demucs_model_set_name_d = 'demucs48_hq-28a1282c.th' elif data['mdx_only_ensem_d'] == "Demucs: Demucs v2": demucs_model_set_name_d = 'demucs-e07c671f.th' elif data['mdx_only_ensem_d'] == "Demucs: Demucs_extra v2": demucs_model_set_name_d = 'demucs_extra-3646af93.th' elif data['mdx_only_ensem_d'] == "Demucs: Demucs_unittest v2": demucs_model_set_name_d = 'demucs_unittest-09ebc15f.th' elif data['mdx_only_ensem_d'] == "Demucs: mdx_extra": demucs_model_set_name_d = 'mdx_extra' elif data['mdx_only_ensem_d'] == "Demucs: mdx_extra_q": demucs_model_set_name_d = 'mdx_extra_q' elif data['mdx_only_ensem_d'] == "Demucs: mdx": demucs_model_set_name_d = 'mdx' elif data['mdx_only_ensem_d'] == "Demucs: mdx_q": demucs_model_set_name_d = 'mdx_q' elif data['mdx_only_ensem_d'] == "Demucs: UVR_Demucs_Model_1": demucs_model_set_name_d = 'UVR_Demucs_Model_1' elif data['mdx_only_ensem_d'] == "Demucs: UVR_Demucs_Model_2": demucs_model_set_name_d = 'UVR_Demucs_Model_2' elif data['mdx_only_ensem_d'] == "Demucs: UVR_Demucs_Model_Bag": demucs_model_set_name_d = 'UVR_Demucs_Model_Bag' else: demucs_model_set_name_d = data['mdx_only_ensem_d'] if data['mdx_only_ensem_e'] == "Demucs: Tasnet v1": demucs_model_set_name_e = 'tasnet.th' elif data['mdx_only_ensem_e'] == "Demucs: Tasnet_extra v1": demucs_model_set_name_e = 'tasnet_extra.th' elif data['mdx_only_ensem_e'] == "Demucs: Demucs v1": demucs_model_set_name_e = 'demucs.th' elif data['mdx_only_ensem_e'] == "Demucs: Demucs_extra v1": demucs_model_set_name_e = 'demucs_extra.th' elif data['mdx_only_ensem_e'] == "Demucs: Light v1": demucs_model_set_name_e = 'light.th' elif data['mdx_only_ensem_e'] == "Demucs: Light_extra v1": demucs_model_set_name_e = 'light_extra.th' elif data['mdx_only_ensem_e'] == "Demucs: Demucs v1.gz": demucs_model_set_name_e = 'demucs.th.gz' elif data['mdx_only_ensem_e'] == "Demucs: Demucs_extra v1.gz": demucs_model_set_name_e = 'demucs_extra.th.gz' elif data['mdx_only_ensem_e'] == "Demucs: Light v1.gz": demucs_model_set_name_e = 'light.th.gz' elif data['mdx_only_ensem_e'] == "Demucs: Light_extra v1.gz": demucs_model_set_name_e = 'light_extra.th.gz' elif data['mdx_only_ensem_e'] == "Demucs: Tasnet v2": demucs_model_set_name_e = 'tasnet-beb46fac.th' elif data['mdx_only_ensem_e'] == "Demucs: Tasnet_extra v2": demucs_model_set_name_e = 'tasnet_extra-df3777b2.th' elif data['mdx_only_ensem_e'] == "Demucs: Demucs48_hq v2": demucs_model_set_name_e = 'demucs48_hq-28a1282c.th' elif data['mdx_only_ensem_e'] == "Demucs: Demucs v2": demucs_model_set_name_e = 'demucs-e07c671f.th' elif data['mdx_only_ensem_e'] == "Demucs: Demucs_extra v2": demucs_model_set_name_e = 'demucs_extra-3646af93.th' elif data['mdx_only_ensem_e'] == "Demucs: Demucs_unittest v2": demucs_model_set_name_e = 'demucs_unittest-09ebc15f.th' elif data['mdx_only_ensem_e'] == "Demucs: mdx_extra": demucs_model_set_name_e = 'mdx_extra' elif data['mdx_only_ensem_e'] == "Demucs: mdx_extra_q": demucs_model_set_name_e = 'mdx_extra_q' elif data['mdx_only_ensem_e'] == "Demucs: mdx": demucs_model_set_name_e = 'mdx' elif data['mdx_only_ensem_e'] == "Demucs: mdx_q": demucs_model_set_name_e = 'mdx_q' elif data['mdx_only_ensem_e'] == "Demucs: UVR_Demucs_Model_1": demucs_model_set_name_e = 'UVR_Demucs_Model_1' elif data['mdx_only_ensem_e'] == "Demucs: UVR_Demucs_Model_2": demucs_model_set_name_e = 'UVR_Demucs_Model_2' elif data['mdx_only_ensem_e'] == "Demucs: UVR_Demucs_Model_Bag": demucs_model_set_name_e = 'UVR_Demucs_Model_Bag' else: demucs_model_set_name_e = data['mdx_only_ensem_e'] # Update default settings global default_window_size global default_agg global normalization_set default_window_size = data['window_size'] default_agg = data['agg'] 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') stime = time.perf_counter() progress_var.set(0) text_widget.clear() button_widget.configure(state=tk.DISABLED) # Disable Button if os.path.exists('models/Main_Models/7_HP2-UVR.pth') \ or os.path.exists('models/Main_Models/8_HP2-UVR.pth') \ or os.path.exists('models/Main_Models/9_HP2-UVR.pth'): hp2_ens = 'on' else: hp2_ens = 'off' timestampnum = round(datetime.utcnow().timestamp()) randomnum = randrange(100000, 1000000) #print('Do all of the HP models exist? ' + hp2_ens) # Separation Preperation try: #Ensemble Dictionary 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'] 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 if not data['ensChoose'] == 'Manual Ensemble': ##### Basic VR Ensemble ##### #1st Model vr_ensem_a_name = data['vr_ensem_a'] vr_ensem_a = f'models/Main_Models/{vr_ensem_a_name}.pth' vr_param_ens_a = data['vr_basic_USER_model_param_1'] #2nd Model vr_ensem_b_name = data['vr_ensem_b'] vr_ensem_b = f'models/Main_Models/{vr_ensem_b_name}.pth' vr_param_ens_b = data['vr_basic_USER_model_param_2'] #3rd Model vr_ensem_c_name = data['vr_ensem_c'] vr_ensem_c = f'models/Main_Models/{vr_ensem_c_name}.pth' vr_param_ens_c = data['vr_basic_USER_model_param_3'] #4th Model vr_ensem_d_name = data['vr_ensem_d'] vr_ensem_d = f'models/Main_Models/{vr_ensem_d_name}.pth' vr_param_ens_d = data['vr_basic_USER_model_param_4'] # 5th Model vr_ensem_e_name = data['vr_ensem_e'] vr_ensem_e = f'models/Main_Models/{vr_ensem_e_name}.pth' vr_param_ens_e = data['vr_basic_USER_model_param_5'] if data['vr_ensem_c'] == 'No Model' and data['vr_ensem_d'] == 'No Model' and data['vr_ensem_e'] == 'No Model': Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_param': vr_param_ens_a, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/2' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_param': vr_param_ens_b, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/2' } ] elif data['vr_ensem_c'] == 'No Model' and data['vr_ensem_d'] == 'No Model': Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_param': vr_param_ens_a, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/3' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_param': vr_param_ens_b, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/3' }, { 'model_name': vr_ensem_e_name, 'model_name_c':vr_ensem_e_name, 'model_param': vr_param_ens_e, 'model_location': vr_ensem_e, 'loop_name': 'Ensemble Mode - Model 3/3' } ] elif data['vr_ensem_c'] == 'No Model' and data['vr_ensem_e'] == 'No Model': Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_param': vr_param_ens_a, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/3' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_param': vr_param_ens_b, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/3' }, { 'model_name': vr_ensem_d_name, 'model_name_c':vr_ensem_d_name, 'model_param': vr_param_ens_d, 'model_location': vr_ensem_d, 'loop_name': 'Ensemble Mode - Model 3/3' } ] elif data['vr_ensem_d'] == 'No Model' and data['vr_ensem_e'] == 'No Model': Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_param': vr_param_ens_a, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/3' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_param': vr_param_ens_b, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/3' }, { 'model_name': vr_ensem_c_name, 'model_name_c':vr_ensem_c_name, 'model_param': vr_param_ens_c, 'model_location': vr_ensem_c, 'loop_name': 'Ensemble Mode - Model 3/3' } ] elif data['vr_ensem_d'] == 'No Model': Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_param': vr_param_ens_a, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/4' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_param': vr_param_ens_b, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/4' }, { 'model_name': vr_ensem_c_name, 'model_name_c':vr_ensem_c_name, 'model_param': vr_param_ens_c, 'model_location': vr_ensem_c, 'loop_name': 'Ensemble Mode - Model 3/4' }, { 'model_name': vr_ensem_e_name, 'model_name_c':vr_ensem_e_name, 'model_param': vr_param_ens_e, 'model_location': vr_ensem_e, 'loop_name': 'Ensemble Mode - Model 4/4' } ] elif data['vr_ensem_c'] == 'No Model': Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_param': vr_param_ens_a, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/4' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_param': vr_param_ens_b, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/4' }, { 'model_name': vr_ensem_d_name, 'model_name_c':vr_ensem_d_name, 'model_param': vr_param_ens_d, 'model_location': vr_ensem_d, 'loop_name': 'Ensemble Mode - Model 3/4' }, { 'model_name': vr_ensem_e_name, 'model_name_c':vr_ensem_e_name, 'model_param': vr_param_ens_e, 'model_location': vr_ensem_e, 'loop_name': 'Ensemble Mode - Model 4/4' } ] elif data['vr_ensem_e'] == 'No Model': Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_param': vr_param_ens_a, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/4' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_param': vr_param_ens_b, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/4' }, { 'model_name': vr_ensem_c_name, 'model_name_c':vr_ensem_c_name, 'model_param': vr_param_ens_c, 'model_location': vr_ensem_c, 'loop_name': 'Ensemble Mode - Model 3/4' }, { 'model_name': vr_ensem_d_name, 'model_name_c':vr_ensem_d_name, 'model_param': vr_param_ens_d, 'model_location': vr_ensem_d, 'loop_name': 'Ensemble Mode - Model 4/4' } ] else: Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_param': vr_param_ens_a, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/5' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_param': vr_param_ens_b, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/5' }, { 'model_name': vr_ensem_c_name, 'model_name_c':vr_ensem_c_name, 'model_param': vr_param_ens_c, 'model_location': vr_ensem_c, 'loop_name': 'Ensemble Mode - Model 3/5' }, { 'model_name': vr_ensem_d_name, 'model_name_c':vr_ensem_d_name, 'model_param': vr_param_ens_d, 'model_location': vr_ensem_d, 'loop_name': 'Ensemble Mode - Model 4/5' }, { 'model_name': vr_ensem_e_name, 'model_name_c':vr_ensem_e_name, 'model_param': vr_param_ens_e, 'model_location': vr_ensem_e, 'loop_name': 'Ensemble Mode - Model 5/5' } ] ##### Multi-AI Ensemble ##### #VR Model 1 vr_ensem_name = data['vr_ensem'] vr_ensem = f'models/Main_Models/{vr_ensem_name}.pth' vr_param_ens_multi = data['vr_multi_USER_model_param_1'] #VR Model 2 vr_ensem_mdx_a_name = data['vr_ensem_mdx_a'] vr_ensem_mdx_a = f'models/Main_Models/{vr_ensem_mdx_a_name}.pth' vr_param_ens_multi_a = data['vr_multi_USER_model_param_2'] #VR Model 3 vr_ensem_mdx_b_name = data['vr_ensem_mdx_b'] vr_ensem_mdx_b = f'models/Main_Models/{vr_ensem_mdx_b_name}.pth' vr_param_ens_multi_b = data['vr_multi_USER_model_param_3'] #VR Model 4 vr_ensem_mdx_c_name = data['vr_ensem_mdx_c'] vr_ensem_mdx_c = f'models/Main_Models/{vr_ensem_mdx_c_name}.pth' vr_param_ens_multi_c = data['vr_multi_USER_model_param_4'] #MDX-Net/Demucs Model 1 if 'MDX-Net:' in data['mdx_ensem']: mdx_model_run_mul_a = 'yes' mdx_net_model_name = data['mdx_ensem'] head, sep, tail = mdx_net_model_name.partition('MDX-Net: ') mdx_net_model_name = tail #mdx_ensem = mdx_net_model_name if mdx_net_model_name == 'UVR-MDX-NET 1': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_1_9703.onnx'): mdx_ensem = 'UVR_MDXNET_1_9703' else: mdx_ensem = 'UVR_MDXNET_9703' elif mdx_net_model_name == 'UVR-MDX-NET 2': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_2_9682.onnx'): mdx_ensem = 'UVR_MDXNET_2_9682' else: mdx_ensem = 'UVR_MDXNET_9682' elif mdx_net_model_name == 'UVR-MDX-NET 3': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_3_9662.onnx'): mdx_ensem = 'UVR_MDXNET_3_9662' else: mdx_ensem = 'UVR_MDXNET_9662' elif mdx_net_model_name == 'UVR-MDX-NET Karaoke': mdx_ensem = 'UVR_MDXNET_KARA' elif mdx_net_model_name == 'UVR-MDX-NET Main': mdx_ensem = 'UVR_MDXNET_Main' else: mdx_ensem = mdx_net_model_name if 'Demucs:' in data['mdx_ensem']: mdx_model_run_mul_a = 'no' mdx_ensem = demucs_model_set_name_muilti_a if data['mdx_ensem'] == 'No Model': mdx_ensem = 'pass' mdx_model_run_mul_a = 'pass' #MDX-Net/Demucs Model 2 if 'MDX-Net:' in data['mdx_ensem_b']: mdx_model_run_mul_b = 'yes' mdx_net_model_name = data['mdx_ensem_b'] head, sep, tail = mdx_net_model_name.partition('MDX-Net: ') mdx_net_model_name = tail #mdx_ensem_b = mdx_net_model_name if mdx_net_model_name == 'UVR-MDX-NET 1': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_1_9703.onnx'): mdx_ensem_b = 'UVR_MDXNET_1_9703' else: mdx_ensem_b = 'UVR_MDXNET_9703' elif mdx_net_model_name == 'UVR-MDX-NET 2': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_2_9682.onnx'): mdx_ensem_b = 'UVR_MDXNET_2_9682' else: mdx_ensem_b = 'UVR_MDXNET_9682' elif mdx_net_model_name == 'UVR-MDX-NET 3': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_3_9662.onnx'): mdx_ensem_b = 'UVR_MDXNET_3_9662' else: mdx_ensem_b = 'UVR_MDXNET_9662' elif mdx_net_model_name == 'UVR-MDX-NET Karaoke': mdx_ensem_b = 'UVR_MDXNET_KARA' elif mdx_net_model_name == 'UVR-MDX-NET Main': mdx_ensem_b = 'UVR_MDXNET_Main' else: mdx_ensem_b = mdx_net_model_name if 'Demucs:' in data['mdx_ensem_b']: mdx_model_run_mul_b = 'no' mdx_ensem_b = demucs_model_set_name_muilti_b if data['mdx_ensem_b'] == 'No Model': mdx_ensem_b = 'pass' mdx_model_run_mul_b = 'pass' if data['vr_ensem'] == 'No Model' and data['vr_ensem_mdx_a'] == 'No Model' and data['vr_ensem_mdx_b'] == 'No Model' and data['vr_ensem_mdx_c'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': mdx_ensem, 'mdx_model_run': mdx_model_run_mul_a, 'model_name_c': vr_ensem_name, 'model_param': vr_param_ens_multi, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {mdx_ensem}', }, { 'model_name': 'pass', 'mdx_model_name': mdx_ensem_b, 'mdx_model_run': mdx_model_run_mul_b, 'model_name_c': 'pass', 'model_param': 'pass', 'model_location':'pass', 'loop_name': f'Ensemble Mode - Last Model - {mdx_ensem_b}', } ] elif data['vr_ensem_mdx_a'] == 'No Model' and data['vr_ensem_mdx_b'] == 'No Model' and data['vr_ensem_mdx_c'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': mdx_ensem, 'mdx_model_run': mdx_model_run_mul_a, 'model_name_c': vr_ensem_name, 'model_param': vr_param_ens_multi, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}', }, { 'model_name': 'pass', 'mdx_model_name': mdx_ensem_b, 'mdx_model_run': mdx_model_run_mul_b, 'model_name_c': 'pass', 'model_param': 'pass', 'model_location':'pass', 'loop_name': 'Ensemble Mode - Last Model', } ] elif data['vr_ensem_mdx_a'] == 'No Model' and data['vr_ensem_mdx_b'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': mdx_ensem_b, 'mdx_model_run': mdx_model_run_mul_b, 'model_name_c': vr_ensem_name, 'model_param': vr_param_ens_multi, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}' }, { 'model_name': vr_ensem_mdx_c_name, 'mdx_model_name': mdx_ensem, 'mdx_model_run': mdx_model_run_mul_a, 'model_name_c': vr_ensem_mdx_c_name, 'model_param': vr_param_ens_multi_c, 'model_location':vr_ensem_mdx_c, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_c_name}' } ] elif data['vr_ensem_mdx_a'] == 'No Model' and data['vr_ensem_mdx_c'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': mdx_ensem_b, 'mdx_model_run': mdx_model_run_mul_b, 'model_name_c': vr_ensem_name, 'model_param': vr_param_ens_multi, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}' }, { 'model_name': vr_ensem_mdx_b_name, 'mdx_model_name': mdx_ensem, 'mdx_model_run': mdx_model_run_mul_a, 'model_name_c': vr_ensem_mdx_b_name, 'model_param': vr_param_ens_multi_b, 'model_location':vr_ensem_mdx_b, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_b_name}' }, ] elif data['vr_ensem_mdx_b'] == 'No Model' and data['vr_ensem_mdx_c'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': mdx_ensem_b, 'mdx_model_run': mdx_model_run_mul_b, 'model_name_c': vr_ensem_name, 'model_param': vr_param_ens_multi, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}' }, { 'model_name': vr_ensem_mdx_a_name, 'mdx_model_name': mdx_ensem, 'mdx_model_run': mdx_model_run_mul_a, 'model_name_c': vr_ensem_mdx_a_name, 'model_param': vr_param_ens_multi_a, 'model_location':vr_ensem_mdx_a, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_a_name}' } ] elif data['vr_ensem_mdx_a'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': 'pass', 'mdx_model_run': 'pass', 'model_name_c': vr_ensem_name, 'model_param': vr_param_ens_multi, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}' }, { 'model_name': vr_ensem_mdx_b_name, 'mdx_model_name': mdx_ensem_b, 'mdx_model_run': mdx_model_run_mul_b, 'model_name_c': vr_ensem_mdx_b_name, 'model_param': vr_param_ens_multi_b, 'model_location':vr_ensem_mdx_b, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_b_name}' }, { 'model_name': vr_ensem_mdx_c_name, 'mdx_model_name': mdx_ensem, 'mdx_model_run': mdx_model_run_mul_a, 'model_name_c': vr_ensem_mdx_c_name, 'model_param': vr_param_ens_multi_c, 'model_location':vr_ensem_mdx_c, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_c_name}' } ] elif data['vr_ensem_mdx_b'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': 'pass', 'mdx_model_run': 'pass', 'model_name_c': vr_ensem_name, 'model_param': vr_param_ens_multi, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}' }, { 'model_name': vr_ensem_mdx_a_name, 'mdx_model_name': mdx_ensem_b, 'mdx_model_run': mdx_model_run_mul_b, 'model_name_c': vr_ensem_mdx_a_name, 'model_param': vr_param_ens_multi_a, 'model_location':vr_ensem_mdx_a, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_a_name}' }, { 'model_name': vr_ensem_mdx_c_name, 'mdx_model_name': mdx_ensem, 'mdx_model_run': mdx_model_run_mul_a, 'model_name_c': vr_ensem_mdx_c_name, 'model_param': vr_param_ens_multi_c, 'model_location':vr_ensem_mdx_c, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_c_name}' } ] elif data['vr_ensem_mdx_c'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': 'pass', 'mdx_model_run': 'pass', 'model_name_c': vr_ensem_name, 'model_param': vr_param_ens_multi, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}' }, { 'model_name': vr_ensem_mdx_a_name, 'mdx_model_name': mdx_ensem_b, 'mdx_model_run': mdx_model_run_mul_b, 'model_name_c': vr_ensem_mdx_a_name, 'model_param': vr_param_ens_multi_a, 'model_location':vr_ensem_mdx_a, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_a_name}' }, { 'model_name': vr_ensem_mdx_b_name, 'mdx_model_name': mdx_ensem, 'mdx_model_run': mdx_model_run_mul_a, 'model_name_c': vr_ensem_mdx_b_name, 'model_param': vr_param_ens_multi_b, 'model_location':vr_ensem_mdx_b, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_b_name}' } ] else: mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': 'pass', 'mdx_model_run': 'pass', 'model_name_c': vr_ensem_name, 'model_param': vr_param_ens_multi, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}' }, { 'model_name': vr_ensem_mdx_a_name, 'mdx_model_name': 'pass', 'mdx_model_run': 'pass', 'model_name_c': vr_ensem_mdx_a_name, 'model_param': vr_param_ens_multi_a, 'model_location':vr_ensem_mdx_a, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_a_name}' }, { 'model_name': vr_ensem_mdx_b_name, 'mdx_model_name': mdx_ensem_b, 'mdx_model_run': mdx_model_run_mul_b, 'model_name_c': vr_ensem_mdx_b_name, 'model_param': vr_param_ens_multi_b, 'model_location':vr_ensem_mdx_b, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_b_name}' }, { 'model_name': vr_ensem_mdx_c_name, 'mdx_model_name': mdx_ensem, 'mdx_model_run': mdx_model_run_mul_a, 'model_name_c': vr_ensem_mdx_c_name, 'model_param': vr_param_ens_multi_c, 'model_location':vr_ensem_mdx_c, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_c_name}' } ] ##### Basic MD Ensemble ##### #MDX-Net/Demucs Model 1 if 'MDX-Net:' in data['mdx_only_ensem_a']: mdx_model_run_a = 'yes' mdx_net_model_name = str(data['mdx_only_ensem_a']) head, sep, tail = mdx_net_model_name.partition('MDX-Net: ') mdx_net_model_name = tail #print('mdx_net_model_name ', mdx_net_model_name) #mdx_only_ensem_a = mdx_net_model_name if mdx_net_model_name == 'UVR-MDX-NET 1': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_1_9703.onnx'): mdx_only_ensem_a = 'UVR_MDXNET_1_9703' else: mdx_only_ensem_a = 'UVR_MDXNET_9703' elif mdx_net_model_name == 'UVR-MDX-NET 2': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_2_9682.onnx'): mdx_only_ensem_a = 'UVR_MDXNET_2_9682' else: mdx_only_ensem_a = 'UVR_MDXNET_9682' elif mdx_net_model_name == 'UVR-MDX-NET 3': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_3_9662.onnx'): mdx_only_ensem_a = 'UVR_MDXNET_3_9662' else: mdx_only_ensem_a = 'UVR_MDXNET_9662' elif mdx_net_model_name == 'UVR-MDX-NET Karaoke': mdx_only_ensem_a = 'UVR_MDXNET_KARA' elif mdx_net_model_name == 'UVR-MDX-NET Main': mdx_only_ensem_a = 'UVR_MDXNET_Main' else: mdx_only_ensem_a = mdx_net_model_name if 'Demucs:' in data['mdx_only_ensem_a']: mdx_model_run_a = 'no' mdx_only_ensem_a = demucs_model_set_name_a if data['mdx_only_ensem_a'] == 'No Model': mdx_model_run_a = 'no' mdx_only_ensem_a = 'pass' #MDX-Net/Demucs Model 2 if 'MDX-Net:' in data['mdx_only_ensem_b']: mdx_model_run_b = 'yes' mdx_net_model_name = str(data['mdx_only_ensem_b']) head, sep, tail = mdx_net_model_name.partition('MDX-Net: ') mdx_net_model_name = tail #mdx_only_ensem_b = mdx_net_model_name if mdx_net_model_name == 'UVR-MDX-NET 1': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_1_9703.onnx'): mdx_only_ensem_b = 'UVR_MDXNET_1_9703' else: mdx_only_ensem_b = 'UVR_MDXNET_9703' elif mdx_net_model_name == 'UVR-MDX-NET 2': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_2_9682.onnx'): mdx_only_ensem_b = 'UVR_MDXNET_2_9682' else: mdx_only_ensem_b = 'UVR_MDXNET_9682' elif mdx_net_model_name == 'UVR-MDX-NET 3': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_3_9662.onnx'): mdx_only_ensem_b = 'UVR_MDXNET_3_9662' else: mdx_only_ensem_b = 'UVR_MDXNET_9662' elif mdx_net_model_name == 'UVR-MDX-NET Karaoke': mdx_only_ensem_b = 'UVR_MDXNET_KARA' elif mdx_net_model_name == 'UVR-MDX-NET Main': mdx_only_ensem_b = 'UVR_MDXNET_Main' else: mdx_only_ensem_b = mdx_net_model_name if 'Demucs:' in data['mdx_only_ensem_b']: mdx_model_run_b = 'no' mdx_only_ensem_b = demucs_model_set_name_b if data['mdx_only_ensem_b'] == 'No Model': mdx_model_run_b = 'no' mdx_only_ensem_b = 'pass' #MDX-Net/Demucs Model 3 if 'MDX-Net:' in data['mdx_only_ensem_c']: mdx_model_run_c = 'yes' mdx_net_model_name = data['mdx_only_ensem_c'] head, sep, tail = mdx_net_model_name.partition('MDX-Net: ') mdx_net_model_name = tail #mdx_only_ensem_c = mdx_net_model_name if mdx_net_model_name == 'UVR-MDX-NET 1': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_1_9703.onnx'): mdx_only_ensem_c = 'UVR_MDXNET_1_9703' else: mdx_only_ensem_c = 'UVR_MDXNET_9703' elif mdx_net_model_name == 'UVR-MDX-NET 2': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_2_9682.onnx'): mdx_only_ensem_c = 'UVR_MDXNET_2_9682' else: mdx_only_ensem_c = 'UVR_MDXNET_9682' elif mdx_net_model_name == 'UVR-MDX-NET 3': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_3_9662.onnx'): mdx_only_ensem_c = 'UVR_MDXNET_3_9662' else: mdx_only_ensem_c = 'UVR_MDXNET_9662' elif mdx_net_model_name == 'UVR-MDX-NET Karaoke': mdx_only_ensem_c = 'UVR_MDXNET_KARA' elif mdx_net_model_name == 'UVR-MDX-NET Main': mdx_only_ensem_c = 'UVR_MDXNET_Main' else: mdx_only_ensem_c = mdx_net_model_name if 'Demucs:' in data['mdx_only_ensem_c']: mdx_model_run_c = 'no' mdx_only_ensem_c = demucs_model_set_name_c if data['mdx_only_ensem_c'] == 'No Model': mdx_model_run_c = 'no' mdx_only_ensem_c = 'pass' #MDX-Net/Demucs Model 4 if 'MDX-Net:' in data['mdx_only_ensem_d']: mdx_model_run_d = 'yes' mdx_net_model_name = data['mdx_only_ensem_d'] head, sep, tail = mdx_net_model_name.partition('MDX-Net: ') mdx_net_model_name = tail #mdx_only_ensem_d = mdx_net_model_name if mdx_net_model_name == 'UVR-MDX-NET 1': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_1_9703.onnx'): mdx_only_ensem_d = 'UVR_MDXNET_1_9703' else: mdx_only_ensem_d = 'UVR_MDXNET_9703' elif mdx_net_model_name == 'UVR-MDX-NET 2': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_2_9682.onnx'): mdx_only_ensem_d = 'UVR_MDXNET_2_9682' else: mdx_only_ensem_d = 'UVR_MDXNET_9682' elif mdx_net_model_name == 'UVR-MDX-NET 3': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_3_9662.onnx'): mdx_only_ensem_d = 'UVR_MDXNET_3_9662' else: mdx_only_ensem_d = 'UVR_MDXNET_9662' elif mdx_net_model_name == 'UVR-MDX-NET Karaoke': mdx_only_ensem_d = 'UVR_MDXNET_KARA' elif mdx_net_model_name == 'UVR-MDX-NET Main': mdx_only_ensem_d = 'UVR_MDXNET_Main' else: mdx_only_ensem_d = mdx_net_model_name if 'Demucs:' in data['mdx_only_ensem_d']: mdx_model_run_d = 'no' mdx_only_ensem_d = demucs_model_set_name_d if data['mdx_only_ensem_d'] == 'No Model': mdx_model_run_d = 'no' mdx_only_ensem_d = 'pass' #MDX-Net/Demucs Model 5 if 'MDX-Net:' in data['mdx_only_ensem_e']: mdx_model_run_e = 'yes' mdx_net_model_name = data['mdx_only_ensem_e'] head, sep, tail = mdx_net_model_name.partition('MDX-Net: ') mdx_net_model_name = tail #mdx_only_ensem_e = mdx_net_model_name if mdx_net_model_name == 'UVR-MDX-NET 1': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_1_9703.onnx'): mdx_only_ensem_e = 'UVR_MDXNET_1_9703' else: mdx_only_ensem_e = 'UVR_MDXNET_9703' elif mdx_net_model_name == 'UVR-MDX-NET 2': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_2_9682.onnx'): mdx_only_ensem_e = 'UVR_MDXNET_2_9682' else: mdx_only_ensem_e = 'UVR_MDXNET_9682' elif mdx_net_model_name == 'UVR-MDX-NET 3': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_3_9662.onnx'): mdx_only_ensem_e = 'UVR_MDXNET_3_9662' else: mdx_only_ensem_e = 'UVR_MDXNET_9662' elif mdx_net_model_name == 'UVR-MDX-NET Karaoke': mdx_only_ensem_e = 'UVR_MDXNET_KARA' elif mdx_net_model_name == 'UVR-MDX-NET Main': mdx_only_ensem_e = 'UVR_MDXNET_Main' else: mdx_only_ensem_e = mdx_net_model_name if 'Demucs:' in data['mdx_only_ensem_e']: mdx_model_run_e = 'no' mdx_only_ensem_e = demucs_model_set_name_e if data['mdx_only_ensem_e'] == 'No Model': mdx_model_run_e = 'no' mdx_only_ensem_e = 'pass' if data['mdx_only_ensem_c'] == 'No Model' and data['mdx_only_ensem_d'] == 'No Model' and data['mdx_only_ensem_e'] == 'No Model': mdx_demuc_only = [ { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_a, 'mdx_model_run': mdx_model_run_a, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_a}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_b, 'mdx_model_run': mdx_model_run_b, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_b}' } ] elif data['mdx_only_ensem_c'] == 'No Model' and data['mdx_only_ensem_d'] == 'No Model': mdx_demuc_only = [ { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_a, 'mdx_model_run': mdx_model_run_a, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_a}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_b, 'mdx_model_run': mdx_model_run_b, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_b}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_e, 'mdx_model_run': mdx_model_run_e, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_e}' } ] elif data['mdx_only_ensem_c'] == 'No Model' and data['mdx_only_ensem_e'] == 'No Model': mdx_demuc_only = [ { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_a, 'mdx_model_run': mdx_model_run_a, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_a}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_b, 'mdx_model_run': mdx_model_run_b, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_b}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_d, 'mdx_model_run': mdx_model_run_d, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_d}' } ] elif data['mdx_only_ensem_d'] == 'No Model' and data['mdx_only_ensem_e'] == 'No Model': mdx_demuc_only = [ { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_a, 'mdx_model_run': mdx_model_run_a, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_a}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_b, 'mdx_model_run': mdx_model_run_b, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_b}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_c, 'mdx_model_run': mdx_model_run_c, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_c}' } ] elif data['mdx_only_ensem_d'] == 'No Model': mdx_demuc_only = [ { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_a, 'mdx_model_run': mdx_model_run_a, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_a}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_b, 'mdx_model_run': mdx_model_run_b, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_b}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_c, 'mdx_model_run': mdx_model_run_c, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_c}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_e, 'mdx_model_run': mdx_model_run_e, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_e}' } ] elif data['mdx_only_ensem_c'] == 'No Model': mdx_demuc_only = [ { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_a, 'mdx_model_run': mdx_model_run_a, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_a}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_b, 'mdx_model_run': mdx_model_run_b, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_b}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_d, 'mdx_model_run': mdx_model_run_d, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_d}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_e, 'mdx_model_run': mdx_model_run_e, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_e}' } ] elif data['mdx_only_ensem_e'] == 'No Model': mdx_demuc_only = [ { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_a, 'mdx_model_run': mdx_model_run_a, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_a}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_b, 'mdx_model_run': mdx_model_run_b, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_b}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_c, 'mdx_model_run': mdx_model_run_c, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_c}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_d, 'mdx_model_run': mdx_model_run_d, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_d}' } ] else: mdx_demuc_only = [ { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_a, 'mdx_model_run': mdx_model_run_a, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_a}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_b, 'mdx_model_run': mdx_model_run_b, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_b}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_c, 'mdx_model_run': mdx_model_run_c, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_c}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_d, 'mdx_model_run': mdx_model_run_d, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_d}' }, { 'model_name': 'pass', 'model_name_c':'pass', 'mdx_model_name': mdx_only_ensem_e, 'mdx_model_run': mdx_model_run_e, 'model_location': 'pass', 'loop_name': f'Ensemble Mode - Running Model - {mdx_only_ensem_e}' } ] if data['ensChoose'] == 'Multi-AI Ensemble': loops = mdx_vr ensefolder = 'Multi_AI_Ensemble_Outputs' ensemode = 'Multi_AI_Ensemble' if data['ensChoose'] == 'Basic VR Ensemble': loops = Basic_Ensem ensefolder = 'Basic_VR_Outputs' ensemode = 'Multi_VR_Ensemble' if data['ensChoose'] == 'Basic MD Ensemble': loops = mdx_demuc_only ensefolder = 'Basic_MDX_Net_Demucs_Ensemble' ensemode = 'Basic_MDX_Net_Demucs_Ensemble' #Prepare Audiofile(s) for file_num, music_file in enumerate(data['input_paths'], start=1): # -Get text and update progress- base_text = get_baseText(total_files=len(data['input_paths']), file_num=file_num) progress_kwargs = {'progress_var': progress_var, 'total_files': len(data['input_paths']), 'file_num': file_num} update_progress(**progress_kwargs, step=0) 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 #Prepare to loop models for i, c in tqdm(enumerate(loops), disable=True, desc='Iterations..'): try: if c['mdx_model_name'] == 'tasnet.th': ModelName_2 = "Tasnet_v1" elif c['mdx_model_name'] == 'tasnet_extra.th': ModelName_2 = "Tasnet_extra_v1" elif c['mdx_model_name'] == 'demucs.th': ModelName_2 = "Demucs_v1" elif c['mdx_model_name'] == 'demucs_extra.th': ModelName_2 = "Demucs_extra_v1" elif c['mdx_model_name'] == 'light_extra.th': ModelName_2 = "Light_v1" elif c['mdx_model_name'] == 'light_extra.th': ModelName_2 = "Light_extra_v1" elif c['mdx_model_name'] == 'tasnet-beb46fac.th': ModelName_2 = "Tasnet_v2" elif c['mdx_model_name'] == 'tasnet_extra-df3777b2.th': ModelName_2 = "Tasnet_extra_v2" elif c['mdx_model_name'] == 'demucs48_hq-28a1282c.th': ModelName_2 = "Demucs48_hq_v2" elif c['mdx_model_name'] == 'demucs-e07c671f.th': ModelName_2 = "Demucs_v2" elif c['mdx_model_name'] == 'demucs_extra-3646af93.th': ModelName_2 = "Demucs_extra_v2" elif c['mdx_model_name'] == 'demucs_unittest-09ebc15f.th': ModelName_2 = "Demucs_unittest_v2" else: ModelName_2 = c['mdx_model_name'] except: pass def determineenseFolderName(): """ Determine the name that is used for the folder and appended to the back of the music files """ enseFolderName = '' if str(ensefolder): enseFolderName += os.path.splitext(os.path.basename(ensefolder))[0] if enseFolderName: try: enseFolderName = '/' + enseFolderName + '_' + str(timestampnum) except: enseFolderName = '/' + enseFolderName + '_' + str(randomnum) return enseFolderName enseFolderName = determineenseFolderName() if enseFolderName: folder_path = f'{data["export_path"]}{enseFolderName}' if not os.path.isdir(folder_path): os.mkdir(folder_path) # Determine File Name base_name = f'{data["export_path"]}{enseFolderName}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}' enseExport = f'{data["export_path"]}{enseFolderName}/' trackname = f'{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}' def get_numbers_from_filename(filename): return re.search(r'\d+', filename).group(0) foldernum = get_numbers_from_filename(enseFolderName) if c['model_location'] == 'pass': pass else: presentmodel = Path(c['model_location']) if presentmodel.is_file(): print(f'The file {presentmodel} exists') else: if data['ensChoose'] == 'Multi-AI Ensemble': text_widget.write(base_text + 'Model "' + c['model_name'] + '.pth" is missing.\n') text_widget.write(base_text + 'This model can be downloaded straight from the \"Settings\" options.\n') text_widget.write(base_text + f'If the error persists, please verify all models are present.\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 else: text_widget.write(base_text + 'Model "' + c['model_name'] + '.pth" is missing.\n') text_widget.write(base_text + 'Installation of v5 Model Expansion Pack required to use this model.\n\n') continue text_widget.write(c['loop_name'] + '\n\n') text_widget.write(base_text + 'Loading ' + c['model_name_c'] + '... ') aggresive_set = float(data['agg']/100) model_size = math.ceil(os.stat(c['model_location']).st_size / 1024) nn_architecture = '{}KB'.format(min(nn_arch_sizes, key=lambda x:abs(x-model_size))) nets = importlib.import_module('lib_v5.nets' + f'_{nn_architecture}'.replace('_{}KB'.format(nn_arch_sizes[0]), ''), package=None) text_widget.write('Done!\n') ModelName=(c['model_location']) ModelParamSettings=(c['model_param']) #Package Models if ModelParamSettings == 'Auto': model_hash = hashlib.md5(open(ModelName,'rb').read()).hexdigest() model_params = [] model_params = lib_v5.filelist.provide_model_param_hash(model_hash) #print(model_params) if model_params[0] == 'Not Found Using Hash': model_params = [] model_params = lib_v5.filelist.provide_model_param_name(ModelName) if model_params[0] == 'Not Found Using Name': text_widget.write(base_text + f'Unable to set model parameters automatically with the selected model. Continue?\n') confirm = tk.messagebox.askyesno(title='Unrecognized Model Detected', message=f'\nThe application could not automatically set the model param for the selected model.\n\n' + f'Would you like to select the Model Param file for this model?\n\n' + f'This model will be skipped if no Model Param is selected.') if confirm: model_param_selection = filedialog.askopenfilename(initialdir='lib_v5/modelparams', title=f'Select Model Param', filetypes=[("Model Param", "*.json")]) model_param_file_path = str(model_param_selection) model_param_file = os.path.splitext(os.path.basename(model_param_file_path))[0] + '.json' model_params = [model_param_file_path, model_param_file] with open(f"lib_v5/filelists/model_cache/vr_param_cache/{model_hash}.txt", 'w') as f: f.write(model_param_file) if model_params[0] == '': text_widget.write(base_text + f'Model param not selected.\n') text_widget.write(base_text + f'Moving on to next model...\n\n') continue else: pass else: text_widget.write(base_text + f'Model param not selected.\n') text_widget.write(base_text + f'Moving on to next model...\n\n') continue else: model_param_file_path = f'lib_v5/modelparams/{ModelParamSettings}' model_params = [model_param_file_path, ModelParamSettings] ModelName_1=(c['model_name']) #print('model param function output ', model_params) print('Model Parameters:', model_params[0]) text_widget.write(base_text + 'Loading assigned model parameters ' + '\"' + model_params[1] + '\"... ') mp = ModelParameters(model_params[0]) text_widget.write('Done!\n') #Load model if os.path.isfile(c['model_location']): device = torch.device('cpu') model = nets.CascadedASPPNet(mp.param['bins'] * 2) model.load_state_dict(torch.load(c['model_location'], map_location=device)) if torch.cuda.is_available() and data['gpu'] >= 0: device = torch.device('cuda:{}'.format(data['gpu'])) model.to(device) model_name = os.path.basename(c["model_name"]) # -Go through the different steps of Separation- # Wave source text_widget.write(base_text + 'Loading audio source... ') X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {} bands_n = len(mp.param['band']) for d in range(bands_n, 0, -1): bp = mp.param['band'][d] if d == bands_n: # high-end band X_wave[d], _ = librosa.load( music_file, bp['sr'], False, dtype=np.float32, res_type=bp['res_type']) if X_wave[d].ndim == 1: X_wave[d] = np.asarray([X_wave[d], X_wave[d]]) else: # lower bands X_wave[d] = librosa.resample(X_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type']) # Stft of wave source X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']) if d == bands_n and data['high_end_process'] != 'none': input_high_end_h = (bp['n_fft']//2 - bp['crop_stop']) + (mp.param['pre_filter_stop'] - mp.param['pre_filter_start']) input_high_end = X_spec_s[d][:, bp['n_fft']//2-input_high_end_h:bp['n_fft']//2, :] text_widget.write('Done!\n') update_progress(**progress_kwargs, step=0.1) text_widget.write(base_text + 'Loading the stft of audio source... ') text_widget.write('Done!\n') text_widget.write(base_text + "Please Wait...\n") X_spec_m = spec_utils.combine_spectrograms(X_spec_s, mp) del X_wave, X_spec_s def inference(X_spec, device, model, aggressiveness): def _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness): model.eval() with torch.no_grad(): preds = [] iterations = [n_window] total_iterations = sum(iterations) text_widget.write(base_text + "Processing "f"{total_iterations} Slices... ") for i in tqdm(range(n_window)): update_progress(**progress_kwargs, step=(0.1 + (0.8/n_window * i))) start = i * roi_size X_mag_window = X_mag_pad[None, :, :, start:start + data['window_size']] X_mag_window = torch.from_numpy(X_mag_window).to(device) pred = model.predict(X_mag_window, aggressiveness) pred = pred.detach().cpu().numpy() preds.append(pred[0]) pred = np.concatenate(preds, axis=2) text_widget.write('Done!\n') return pred def preprocess(X_spec): X_mag = np.abs(X_spec) X_phase = np.angle(X_spec) return X_mag, X_phase X_mag, X_phase = preprocess(X_spec) coef = X_mag.max() X_mag_pre = X_mag / coef n_frame = X_mag_pre.shape[2] pad_l, pad_r, roi_size = dataset.make_padding(n_frame, data['window_size'], model.offset) n_window = int(np.ceil(n_frame / roi_size)) X_mag_pad = np.pad( X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant') pred = _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness) pred = pred[:, :, :n_frame] if data['tta']: pad_l += roi_size // 2 pad_r += roi_size // 2 n_window += 1 X_mag_pad = np.pad( X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant') pred_tta = _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness) pred_tta = pred_tta[:, :, roi_size // 2:] pred_tta = pred_tta[:, :, :n_frame] return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.j * X_phase) else: return pred * coef, X_mag, np.exp(1.j * X_phase) aggressiveness = {'value': aggresive_set, 'split_bin': mp.param['band'][1]['crop_stop']} if data['tta']: text_widget.write(base_text + "Running Inferences (TTA)... \n") else: text_widget.write(base_text + "Running Inference... \n") pred, X_mag, X_phase = inference(X_spec_m, device, model, aggressiveness) # update_progress(**progress_kwargs, # step=0.8) # Postprocess if data['postprocess']: try: text_widget.write(base_text + 'Post processing...') pred_inv = np.clip(X_mag - pred, 0, np.inf) pred = spec_utils.mask_silence(pred, pred_inv) text_widget.write(' Done!\n') except Exception as e: text_widget.write('\n' + base_text + 'Post process failed, check error log.\n') text_widget.write(base_text + 'Moving on...\n') traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while attempting to run Post Processing on "{os.path.basename(music_file)}":\n' + f'Process Method: Ensemble Mode\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 # Inverse stft # nopep8 y_spec_m = pred * X_phase v_spec_m = X_spec_m - y_spec_m if data['voc_only']: pass else: text_widget.write(base_text + 'Saving Instrumental... ') if data['high_end_process'].startswith('mirroring'): input_high_end_ = spec_utils.mirroring(data['high_end_process'], y_spec_m, input_high_end, mp) wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp, input_high_end_h, input_high_end_) if data['voc_only']: pass else: text_widget.write('Done!\n') else: wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp) if data['voc_only']: pass else: text_widget.write('Done!\n') if data['inst_only']: pass else: text_widget.write(base_text + 'Saving Vocals... ') if data['high_end_process'].startswith('mirroring'): input_high_end_ = spec_utils.mirroring(data['high_end_process'], v_spec_m, input_high_end, mp) wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp, input_high_end_h, input_high_end_) if data['inst_only']: pass else: text_widget.write('Done!\n') else: wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp) if data['inst_only']: pass else: text_widget.write('Done!\n') update_progress(**progress_kwargs, step=1) # Save output music files save_files(wav_instrument, wav_vocals) # Save output image if data['output_image']: with open('{}_{}_Instruments.jpg'.format(base_name, c['model_name']), mode='wb') as f: image = spec_utils.spectrogram_to_image(y_spec_m) _, bin_image = cv2.imencode('.jpg', image) bin_image.tofile(f) with open('{}_{}_Vocals.jpg'.format(base_name, c['model_name']), mode='wb') as f: image = spec_utils.spectrogram_to_image(v_spec_m) _, bin_image = cv2.imencode('.jpg', image) bin_image.tofile(f) text_widget.write(base_text + 'Completed Separation!\n\n') ################################### if data['ensChoose'] == 'Multi-AI Ensemble' or data['ensChoose'] == 'Basic MD Ensemble': if data['demucsmodel']: demucs_switch = 'on' else: demucs_switch = 'off' if data['demucs_only']: demucs_only = 'on' else: demucs_only = 'off' if c['mdx_model_name'] == 'tasnet.th': post_mdx_name = "Tasnet v1" elif c['mdx_model_name'] == 'tasnet_extra.th': post_mdx_name = "Tasnet_extra v1" elif c['mdx_model_name'] == 'demucs.th': post_mdx_name = "Demucs v1" elif c['mdx_model_name'] == 'demucs_extra.th': post_mdx_name = "Demucs_extra v1" elif c['mdx_model_name'] == 'light_extra.th': post_mdx_name = "Light v1" elif c['mdx_model_name'] == 'light_extra.th': post_mdx_name = "Light_extra v1" elif c['mdx_model_name'] == 'tasnet-beb46fac.th': post_mdx_name = "Tasnet v2" elif c['mdx_model_name'] == 'tasnet_extra-df3777b2.th': post_mdx_name = "Tasnet_extra v2" elif c['mdx_model_name'] == 'demucs48_hq-28a1282c.th': post_mdx_name = "Demucs48_hq v2" elif c['mdx_model_name'] == 'demucs-e07c671f.th': post_mdx_name = "Demucs v2" elif c['mdx_model_name'] == 'demucs_extra-3646af93.th': post_mdx_name = "Demucs_extra v2" elif c['mdx_model_name'] == 'demucs_unittest-09ebc15f.th': post_mdx_name = "Demucs_unittest v2" else: post_mdx_name = c['mdx_model_name'] mdx_name = c['mdx_model_name'] if c['mdx_model_name'] == 'pass': pass else: text_widget.write('Ensemble Mode - Running Model - ' + post_mdx_name + '\n\n') if c['mdx_model_run'] == 'no': if 'UVR' in mdx_name: demucs_only = 'on' demucs_switch = 'on' demucs_model_set = mdx_name model_set = '' model_set_name = 'UVR' modeltype = 'v' noise_pro = 'MDX-NET_Noise_Profile_14_kHz' stemset_n = '(Vocals)' else: demucs_only = 'on' demucs_switch = 'on' demucs_model_set = mdx_name model_set = '' model_set_name = 'extra' modeltype = 'v' noise_pro = 'MDX-NET_Noise_Profile_14_kHz' stemset_n = '(Vocals)' if c['mdx_model_run'] == 'yes': demucs_only = 'off' model_set = f"{mdx_name}.onnx" model_set_name = mdx_name demucs_model_set = demucs_model_set_name mdx_model_path = f'models/MDX_Net_Models/{mdx_name}.onnx' model_hash = hashlib.md5(open(mdx_model_path,'rb').read()).hexdigest() model_params_mdx = [] model_params_mdx = lib_v5.filelist.provide_mdx_model_param_name(model_hash) modeltype = model_params_mdx[0] noise_pro = model_params_mdx[1] stemset_n = model_params_mdx[2] if autocompensate: compensate = model_params_mdx[3] else: compensate = data['compensate'] source_val = model_params_mdx[4] n_fft_scale_set = model_params_mdx[5] dim_f_set = model_params_mdx[6] #print(model_params_mdx) print('demucs_only? ', demucs_only) if data['noise_pro_select'] == 'Auto Select': noise_pro_set = noise_pro else: noise_pro_set = data['noise_pro_select'] update_progress(**progress_kwargs, step=0) 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') e = os.path.join(data["export_path"]) pred = Predictor() if c['mdx_model_run'] == 'yes': if stemset_n == '(Bass)' or stemset_n == '(Drums)' or stemset_n == '(Other)': widget_text.write(base_text + 'Only vocal and instrumental MDX-Net models are supported in \nensemble mode.\n') widget_text.write(base_text + 'Moving on to next model...\n\n') continue if modeltype == 'Not Set' or \ noise_pro == 'Not Set' or \ stemset_n == 'Not Set' or \ compensate == '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(base_text + 'An unrecognized model has been detected.\n') text_widget.write(base_text + 'Please configure the ONNX model settings accordingly and try again.\n') text_widget.write(base_text + 'Moving on to next model...\n\n') continue pred.prediction_setup() # split pred.prediction( m=music_file, ) else: pass # Emsembling Outputs def get_files(folder="", prefix="", suffix=""): return [f"{folder}{i}" for i in os.listdir(folder) if i.startswith(prefix) if i.endswith(suffix)] if data['appendensem'] == False: if data['settest']: voc_inst = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav"), 'output':'{}_{}_(Instrumental)'.format(foldernum, trackname), 'type': 'Instrumentals' }, { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav"), 'output': '{}_{}_(Vocals)'.format(foldernum, trackname), 'type': 'Vocals' } ] inst = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav"), 'output':'{}_{}_(Instrumental)'.format(foldernum, trackname), 'type': 'Instrumentals' } ] vocal = [ { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav"), 'output': '{}_{}_(Vocals)'.format(foldernum, trackname), 'type': 'Vocals' } ] else: voc_inst = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav"), 'output':'{}_(Instrumental)'.format(trackname), 'type': 'Instrumentals' }, { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav"), 'output': '{}_(Vocals)'.format(trackname), 'type': 'Vocals' } ] inst = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav"), 'output':'{}_(Instrumental)'.format(trackname), 'type': 'Instrumentals' } ] vocal = [ { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav"), 'output': '{}_(Vocals)'.format(trackname), 'type': 'Vocals' } ] else: if data['settest']: voc_inst = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav"), 'output':'{}_{}_Ensembled_{}_(Instrumental)'.format(foldernum, trackname, ensemode), 'type': 'Instrumentals' }, { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav"), 'output': '{}_{}_Ensembled_{}_(Vocals)'.format(foldernum, trackname, ensemode), 'type': 'Vocals' } ] inst = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav"), 'output':'{}_{}_Ensembled_{}_(Instrumental)'.format(foldernum, trackname, ensemode), 'type': 'Instrumentals' } ] vocal = [ { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav"), 'output': '{}_{}_Ensembled_{}_(Vocals)'.format(foldernum, trackname, ensemode), 'type': 'Vocals' } ] else: voc_inst = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav"), 'output':'{}_Ensembled_{}_(Instrumental)'.format(trackname, ensemode), 'type': 'Instrumentals' }, { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav"), 'output': '{}_Ensembled_{}_(Vocals)'.format(trackname, ensemode), 'type': 'Vocals' } ] inst = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav"), 'output':'{}_Ensembled_{}_(Instrumental)'.format(trackname, ensemode), 'type': 'Instrumentals' } ] vocal = [ { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav"), 'output': '{}_Ensembled_{}_(Vocals)'.format(trackname, ensemode), 'type': 'Vocals' } ] if data['voc_only']: ensembles = vocal elif data['inst_only']: ensembles = inst else: ensembles = voc_inst try: for i, e in tqdm(enumerate(ensembles), desc="Ensembling..."): text_widget.write(base_text + "Ensembling " + e['type'] + "... ") 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 sf.write(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])), normalization_set(spec_utils.cmb_spectrogram_to_wave(spec_utils.ensembling(e['algorithm'], specs), mp)), mp.param['sr'], subtype=wav_type_set) if data['saveFormat'] == 'Mp3': try: musfile = pydub.AudioSegment.from_wav(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output']))) musfile.export((os.path.join('{}'.format(data['export_path']),'{}.mp3'.format(e['output']))), format="mp3", bitrate=mp3_bit_set) os.remove((os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])))) 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: text_widget.write('\n' + base_text + 'Failed to save output(s) as Mp3(s).\n') text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') text_widget.write(base_text + 'Moving on... ') else: text_widget.write('\n' + base_text + 'Failed to save output(s) as Mp3(s).\n') text_widget.write(base_text + 'Please check error log.\n') text_widget.write(base_text + 'Moving on... ') 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: Ensemble Mode\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: musfile = pydub.AudioSegment.from_wav(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output']))) musfile.export((os.path.join('{}'.format(data['export_path']),'{}.flac'.format(e['output']))), format="flac") os.remove((os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])))) 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: text_widget.write('\n' + base_text + 'Failed to save output(s) as Flac(s).\n') text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') text_widget.write(base_text + 'Moving on... ') else: text_widget.write('\n' + base_text + 'Failed to save output(s) as Flac(s).\n') text_widget.write(base_text + 'Please check error log.\n') text_widget.write(base_text + 'Moving on... ') 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' + f'Process Method: Ensemble Mode\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 text_widget.write("Done!\n") except: text_widget.write('\n' + base_text + 'Not enough files to ensemble.') pass update_progress(**progress_kwargs, step=0.95) text_widget.write("\n") try: if not data['save']: # Deletes all outputs if Save All Outputs isn't checked files = get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav") for file in files: os.remove(file) if not data['save']: files = get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav") for file in files: os.remove(file) except: pass if data['save'] and data['saveFormat'] == 'Mp3': try: text_widget.write(base_text + 'Saving all ensemble outputs in Mp3... ') path = enseExport #Change working directory os.chdir(path) audio_files = os.listdir() for file in audio_files: #spliting the file into the name and the extension name, ext = os.path.splitext(file) if ext == ".wav": if trackname in file: musfile = pydub.AudioSegment.from_wav(file) #rename them using the old name + ".wav" musfile.export("{0}.mp3".format(name), format="mp3", bitrate=mp3_bit_set) try: files = get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav") for file in files: os.remove(file) except: pass try: files = get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav") for file in files: os.remove(file) except: pass text_widget.write('Done!\n\n') base_path = os.path.dirname(os.path.abspath(__file__)) os.chdir(base_path) except Exception as e: base_path = os.path.dirname(os.path.abspath(__file__)) os.chdir(base_path) traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' if "ffmpeg" in errmessage: text_widget.write('\n' + base_text + 'Failed to save output(s) as Mp3(s).\n') text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') text_widget.write(base_text + 'Moving on...\n') else: text_widget.write('\n' + base_text + 'Failed to save output(s) as Mp3(s).\n') text_widget.write(base_text + 'Please check error log.\n') text_widget.write(base_text + 'Moving on...\n') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'\nError Received while attempting to save ensembled outputs as mp3s.\n' + f'Process Method: Ensemble Mode\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['save'] and data['saveFormat'] == 'Flac': try: text_widget.write(base_text + 'Saving all ensemble outputs in Flac... ') path = enseExport #Change working directory os.chdir(path) audio_files = os.listdir() for file in audio_files: #spliting the file into the name and the extension name, ext = os.path.splitext(file) if ext == ".wav": if trackname in file: musfile = pydub.AudioSegment.from_wav(file) #rename them using the old name + ".wav" musfile.export("{0}.flac".format(name), format="flac") try: files = get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav") for file in files: os.remove(file) except: pass try: files = get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav") for file in files: os.remove(file) except: pass text_widget.write('Done!\n\n') base_path = os.path.dirname(os.path.abspath(__file__)) os.chdir(base_path) except Exception as e: base_path = os.path.dirname(os.path.abspath(__file__)) os.chdir(base_path) traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' if "ffmpeg" in errmessage: text_widget.write('\n' + base_text + 'Failed to save output(s) as Flac(s).\n') text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') text_widget.write(base_text + 'Moving on...\n') else: text_widget.write('\n' + base_text + 'Failed to save output(s) as Flac(s).\n') text_widget.write(base_text + 'Please check error log.\n') text_widget.write(base_text + 'Moving on...\n') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'\nError Received while attempting to ensembled outputs as Flacs.\n' + f'Process Method: Ensemble Mode\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 try: os.remove('temp.wav') except: pass if len(os.listdir(enseExport)) == 0: #Check if the folder is empty shutil.rmtree(folder_path) #Delete folder if empty else: progress_kwargs = {'progress_var': progress_var, 'total_files': len(data['input_paths']), 'file_num': len(data['input_paths'])} base_text = get_baseText(total_files=len(data['input_paths']), file_num=len(data['input_paths'])) try: 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 music_file = data['input_paths'] if len(data['input_paths']) <= 1: text_widget.write(base_text + "Not enough files to process.\n") pass else: update_progress(**progress_kwargs, step=0.2) savefilename = (data['input_paths'][0]) trackname1 = f'{os.path.splitext(os.path.basename(savefilename))[0]}' timestampnum = round(datetime.utcnow().timestamp()) randomnum = randrange(100000, 1000000) if data['settest']: try: insts = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output':'{}_{}_Manual_Ensemble_(Min Spec)'.format(timestampnum, trackname1), 'type': 'Instrumentals' } ] vocals = [ { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output': '{}_{}_Manual_Ensemble_(Max Spec)'.format(timestampnum, trackname1), 'type': 'Vocals' } ] invert_spec = [ { 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output': '{}_{}_diff_si'.format(timestampnum, trackname1), 'type': 'Spectral Inversion' } ] invert_nor = [ { 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output': '{}_{}_diff_ni'.format(timestampnum, trackname1), 'type': 'Normal Inversion' } ] except: insts = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output':'{}_{}_Manual_Ensemble_(Min Spec)'.format(randomnum, trackname1), 'type': 'Instrumentals' } ] vocals = [ { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output': '{}_{}_Manual_Ensemble_(Max Spec)'.format(randomnum, trackname1), 'type': 'Vocals' } ] invert_spec = [ { 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output': '{}_{}_diff_si'.format(randomnum, trackname1), 'type': 'Spectral Inversion' } ] invert_nor = [ { 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output': '{}_{}_diff_ni'.format(randomnum, trackname1), 'type': 'Normal Inversion' } ] else: insts = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output':'{}_Manual_Ensemble_(Min Spec)'.format(trackname1), 'type': 'Instrumentals' } ] vocals = [ { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output': '{}_Manual_Ensemble_(Max Spec)'.format(trackname1), 'type': 'Vocals' } ] invert_spec = [ { 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output': '{}_diff_si'.format(trackname1), 'type': 'Spectral Inversion' } ] invert_nor = [ { 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output': '{}_diff_ni'.format(trackname1), 'type': 'Normal Inversion' } ] if data['algo'] == 'Instrumentals (Min Spec)': ensem = insts if data['algo'] == 'Vocals (Max Spec)': ensem = vocals if data['algo'] == 'Invert (Spectral)': ensem = invert_spec if data['algo'] == 'Invert (Normal)': ensem = invert_nor #Prepare to loop models if data['algo'] == 'Instrumentals (Min Spec)' or data['algo'] == 'Vocals (Max Spec)': for i, e in tqdm(enumerate(ensem), desc="Ensembling..."): text_widget.write(base_text + "Ensembling " + e['type'] + "... ") wave, specs = {}, {} mp = ModelParameters(e['model_params']) for i in range(len(data['input_paths'])): 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( data['input_paths'][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 sf.write(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])), normalization_set(spec_utils.cmb_spectrogram_to_wave(spec_utils.ensembling(e['algorithm'], specs), mp)), mp.param['sr'], subtype=wav_type_set) if data['saveFormat'] == 'Mp3': try: musfile = pydub.AudioSegment.from_wav(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output']))) musfile.export((os.path.join('{}'.format(data['export_path']),'{}.mp3'.format(e['output']))), format="mp3", bitrate=mp3_bit_set) os.remove((os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])))) except Exception as e: text_widget.write('\n' + base_text + 'Failed to save output(s) as Mp3.') text_widget.write('\n' + base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') text_widget.write(base_text + 'Moving on...\n') text_widget.write(base_text + f'Complete!\n') traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while attempting to run Manual Ensemble:\n' + f'Process Method: Ensemble Mode\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 progress_var.set(0) button_widget.configure(state=tk.NORMAL) return if data['saveFormat'] == 'Flac': try: musfile = pydub.AudioSegment.from_wav(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output']))) musfile.export((os.path.join('{}'.format(data['export_path']),'{}.flac'.format(e['output']))), format="flac") os.remove((os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])))) except Exception as e: text_widget.write('\n' + base_text + 'Failed to save output as Flac.\n') text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') text_widget.write(base_text + 'Moving on...\n') text_widget.write(base_text + f'Complete!\n') traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while attempting to run Manual Ensemble:\n' + f'Process Method: Ensemble Mode\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 progress_var.set(0) button_widget.configure(state=tk.NORMAL) return text_widget.write("Done!\n") if data['algo'] == 'Invert (Spectral)' and data['algo'] == 'Invert (Normal)': if len(data['input_paths']) != 2: text_widget.write(base_text + "Invalid file count.\n") pass else: for i, e in tqdm(enumerate(ensem), desc="Inverting..."): wave, specs = {}, {} mp = ModelParameters(e['model_params']) for i in range(len(data['input_paths'])): 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( data['input_paths'][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] if data['algo'] == 'Invert (Spectral)': text_widget.write(base_text + "Performing " + e['type'] + "... ") 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])) sf.write(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])), spec_utils.cmb_spectrogram_to_wave(-v_spec, mp), mp.param['sr'], subtype=wav_type_set) if data['algo'] == 'Invert (Normal)': v_spec = specs[0] - specs[1] sf.write(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])), spec_utils.cmb_spectrogram_to_wave(v_spec, mp), mp.param['sr'], subtype=wav_type_set) text_widget.write("Done!\n") 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: Ensemble Mode\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: Ensemble Mode\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: Ensemble Mode\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: Ensemble Mode\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: Ensemble Mode\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: Ensemble Mode\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: Ensemble Mode\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: Ensemble Mode\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"\nFor raw error details, go to the Error Log tab in the Help Guide.\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: Ensemble Mode\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: Ensemble Mode\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 enex_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 locate a model you selected for this ensemble.\n') text_widget.write(f'\nPlease do the following to use all compatible models:\n\n1. Navigate to the \"Updates\" tab in the Help Guide.\n2. Download and install the v5 Model Expansion Pack.\n3. Then try again.\n\n') text_widget.write(f'If the error persists, please verify all models are present.\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: Ensemble Mode\n\n' + f'The application was unable to locate a model you selected for this ensemble.\n' + f'\nPlease do the following to use all compatible models:\n\n1. Navigate to the \"Updates\" tab in the Help Guide.\n2. Download and install the model expansion pack.\n3. Then try again.\n\n' + f'If the error persists, please verify all models are present.\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: Ensemble Mode\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: Ensemble Mode\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("\nFor raw error details, go to the Error Log tab in the Help Guide.\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)))}') torch.cuda.empty_cache() button_widget.configure(state=tk.NORMAL) # Enable Button return update_progress(**progress_kwargs, step=1) print('Done!') progress_var.set(0) if not data['ensChoose'] == 'Manual Ensemble': text_widget.write(base_text + f'Conversions Completed!\n') elif data['algo'] == 'Instrumentals (Min Spec)' and len(data['input_paths']) <= 1 or data['algo'] == 'Vocals (Max Spec)' and len(data['input_paths']) <= 1: text_widget.write(base_text + f'Please select 2 or more files to use this feature and try again.\n') elif data['algo'] == 'Instrumentals (Min Spec)' or data['algo'] == 'Vocals (Max Spec)': text_widget.write(base_text + f'Ensemble Complete!\n') elif len(data['input_paths']) != 2 and data['algo'] == 'Invert (Spectral)' or len(data['input_paths']) != 2 and data['algo'] == 'Invert (Normal)': text_widget.write(base_text + f'Please select exactly 2 files to extract difference.\n') elif data['algo'] == 'Invert (Spectral)' or data['algo'] == 'Invert (Normal)': text_widget.write(base_text + f'Complete!\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