From f019ce2df4ad2d9583cd91b8a494b982470bf42c Mon Sep 17 00:00:00 2001 From: Anjok07 <68268275+Anjok07@users.noreply.github.com> Date: Wed, 2 Nov 2022 03:24:40 -0500 Subject: [PATCH] Delete inference_v5_ensemble.py --- inference_v5_ensemble.py | 4681 -------------------------------------- 1 file changed, 4681 deletions(-) delete mode 100644 inference_v5_ensemble.py diff --git a/inference_v5_ensemble.py b/inference_v5_ensemble.py deleted file mode 100644 index 85a0f40..0000000 --- a/inference_v5_ensemble.py +++ /dev/null @@ -1,4681 +0,0 @@ -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) - - mdx_model_set = Toplevel() - - mdx_model_set.geometry("490x515") - window_height = 490 - window_width = 515 - - mdx_model_set.title("Specify Parameters") - - mdx_model_set.resizable(False, False) # This code helps to disable windows from resizing - - screen_width = mdx_model_set.winfo_screenwidth() - screen_height = mdx_model_set.winfo_screenheight() - - x_cordinate = int((screen_width/2) - (window_width/2)) - y_cordinate = int((screen_height/2) - (window_height/2)) - - mdx_model_set.geometry("{}x{}+{}+{}".format(window_width, window_height, x_cordinate, y_cordinate)) - - # change title bar icon - mdx_model_set.iconbitmap('img\\UVR-Icon-v2.ico') - - mdx_model_set_window = ttk.Notebook(mdx_model_set) - - mdx_model_set_window.pack(expand = 1, fill ="both") - - mdx_model_set_window.grid_rowconfigure(0, weight=1) - mdx_model_set_window.grid_columnconfigure(0, weight=1) - - frame0=Frame(mdx_model_set_window,highlightbackground='red',highlightthicknes=0) - frame0.grid(row=0,column=0,padx=0,pady=0) - - frame0.tkraise(frame0) - - space_small = ' '*20 - space_small_1 = ' '*10 - - l0=tk.Label(frame0, text=f'{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 - self.okVar.set(1) - stop_button() - mdx_model_set.destroy() - return - - l0=ttk.Button(frame0,text="Stop Process", command=stop) - l0.grid(row=13,column=1,padx=0,pady=30) - - #print('print from mdx_model_set ', model_hash) - - #source_val = 0 - - mdx_model_set.protocol("WM_DELETE_WINDOW", stop) - - frame0.wait_variable(self.okVar) - - global n_fft_scale_set - global dim_f_set - global modeltype - global stemset_n - global 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: - if demucs_switch == 'on': - widget_text.write(base_text + 'The selected Demucs model cannot be used with this model.\n') - widget_text.write(base_text + 'Only 2 stem Demucs models are compatible with this model.\n') - widget_text.write(base_text + 'Setting Demucs model to \"UVR_Demucs_Model_1\".\n\n') - demucs_model_set = 'UVR_Demucs_Model_1' - - mdx_model_set.destroy() - - def prediction_setup(self): - - global device - - if data['gpu'] >= 0: - device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') - if data['gpu'] == -1: - device = torch.device('cpu') - - if 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') - if not data['segment'] == 'Default': - widget_text.write(base_text + 'Note: Segments only available for Demucs v3\n') - else: - pass - - 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') - if not data['segment'] == 'Default': - widget_text.write(base_text + 'Note: Segments only available for Demucs v3\n') - else: - pass - 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") - - if data['segment'] == 'Default': - segment = None - if isinstance(self.demucs, BagOfModels): - if segment is not None: - for sub in self.demucs.models: - sub.segment = segment - else: - if segment is not None: - sub.segment = segment - else: - try: - segment = int(data['segment']) - if isinstance(self.demucs, BagOfModels): - if segment is not None: - for sub in self.demucs.models: - sub.segment = segment - else: - if segment is not None: - sub.segment = segment - if split_mode: - widget_text.write(base_text + "Segments set to "f"{segment}.\n") - except: - segment = None - if isinstance(self.demucs, BagOfModels): - if segment is not None: - for sub in self.demucs.models: - sub.segment = segment - else: - if segment is not None: - sub.segment = segment - - self.onnx_models = {} - c = 0 - - 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): - - if data['chunks'] == 'Full': - chunk_set = 0 - widget_text.write(base_text + "Chunk size user-set to \"Full\"... \n") - elif data['chunks'] == 'Auto': - if data['gpu'] == 0: - try: - gpu_mem = round(torch.cuda.get_device_properties(0).total_memory/1.074e+9) - except: - widget_text.write(base_text + 'NVIDIA GPU Required for conversion!\n') - data['gpu'] = -1 - pass - if int(gpu_mem) <= int(6): - chunk_set = int(5) - if demucs_only == 'on': - if no_chunk_demucs: - widget_text.write(base_text + 'Chunk size auto-set to 5... \n') - else: - 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) - if demucs_only == 'on': - if no_chunk_demucs: - widget_text.write(base_text + 'Chunk size auto-set to 10... \n') - else: - widget_text.write(base_text + 'Chunk size auto-set to 10... \n') - if int(gpu_mem) >= int(16): - chunk_set = int(40) - if demucs_only == 'on': - if no_chunk_demucs: - widget_text.write(base_text + 'Chunk size auto-set to 40... \n') - else: - 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) - if demucs_only == 'on': - if no_chunk_demucs: - widget_text.write(base_text + 'Chunk size auto-set to 1... \n') - else: - widget_text.write(base_text + 'Chunk size auto-set to 1... \n') - if sys_mem in [5, 6, 7, 8]: - chunk_set = int(10) - if demucs_only == 'on': - if no_chunk_demucs: - widget_text.write(base_text + 'Chunk size auto-set to 10... \n') - else: - 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) - if demucs_only == 'on': - if no_chunk_demucs: - widget_text.write(base_text + 'Chunk size auto-set to 25... \n') - else: - widget_text.write(base_text + 'Chunk size auto-set to 25... \n') - - if int(sys_mem) >= int(17): - chunk_set = int(60) - if demucs_only == 'on': - if no_chunk_demucs: - widget_text.write(base_text + 'Chunk size auto-set to 60... \n') - else: - widget_text.write(base_text + 'Chunk size auto-set to 60... \n') - elif data['chunks'] == '0': - chunk_set = 0 - if demucs_only == 'on': - if no_chunk_demucs: - widget_text.write(base_text + "Chunk size user-set to \"Full\"... \n") - else: - widget_text.write(base_text + "Chunk size user-set to \"Full\"... \n") - else: - chunk_set = int(data['chunks']) - if demucs_only == 'on': - if no_chunk_demucs: - widget_text.write(base_text + "Chunk size user-set to "f"{chunk_set}... \n") - else: - 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: - if no_chunk_demucs == False: - sources = self.demix_demucs_v1_split(mix) - if no_chunk_demucs == True: - 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: - if no_chunk_demucs == False: - sources = self.demix_demucs_v2_split(mix) - if no_chunk_demucs == True: - sources = self.demix_demucs_v2(segmented_mix, margin_size=margin) - else: - if no_chunk_demucs == False: - sources = self.demix_demucs_split(mix) - if no_chunk_demucs == True: - sources = self.demix_demucs(segmented_mix, margin_size=margin) - else: # both, apply spec effects - base_out = self.demix_base(segmented_mix, margin_size=margin) - if '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: - if no_chunk_demucs == False: - demucs_out = self.demix_demucs_v1_split(mix) - if no_chunk_demucs == True: - demucs_out = self.demix_demucs_v1(segmented_mix, margin_size=margin) - 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 no_chunk_demucs == False: - demucs_out = self.demix_demucs_v2_split(mix) - if no_chunk_demucs == True: - demucs_out = self.demix_demucs_v2(segmented_mix, margin_size=margin) - else: - if no_chunk_demucs == False: - demucs_out = self.demix_demucs_split(mix) - if no_chunk_demucs == True: - demucs_out = self.demix_demucs(segmented_mix, margin_size=margin) - nan_count = np.count_nonzero(np.isnan(demucs_out)) + np.count_nonzero(np.isnan(base_out)) - if nan_count > 0: - print('Warning: there are {} nan values in the array(s).'.format(nan_count)) - demucs_out, base_out = np.nan_to_num(demucs_out), np.nan_to_num(base_out) - sources = {} - - 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 - progress_bar = 0 - - print(' Running ONNX Inference...') - - if onnxitera == 1: - widget_text.write(base_text + f"Running ONNX Inference... ") - else: - widget_text.write(base_text + f"Running ONNX Inference...{space}\n") - - for mix in mixes: - gui_progress_bar_onnx += 1 - if data['demucsmodel']: - update_progress(**progress_kwargs, - step=(0.1 + (0.5/onnxitera_calc * gui_progress_bar_onnx))) - else: - update_progress(**progress_kwargs, - step=(0.1 + (0.8/onnxitera * gui_progress_bar_onnx))) - - progress_bar += 100 - step = (progress_bar / onnxitera) - - if onnxitera == 1: - pass - else: - percent_prog = f"{base_text}MDX-Net Inference Progress: {gui_progress_bar_onnx}/{onnxitera} | {round(step)}%" - widget_text.percentage(percent_prog) - - cmix = mixes[mix] - sources = [] - n_sample = cmix.shape[1] - - mod = 0 - for model in self.models: - mod += 1 - trim = model.n_fft//2 - gen_size = model.chunk_size-2*trim - pad = gen_size - n_sample%gen_size - mix_p = np.concatenate((np.zeros((2,trim)), cmix, np.zeros((2,pad)), np.zeros((2,trim))), 1) - mix_waves = [] - i = 0 - while i < n_sample + pad: - waves = np.array(mix_p[:, i:i+model.chunk_size]) - mix_waves.append(waves) - i += gen_size - mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu) - with torch.no_grad(): - _ort = self.onnx_models[mod] - spek = model.stft(mix_waves) - - tar_waves = model.istft(torch.tensor(_ort.run(None, {'input': spek.cpu().numpy()})[0]))#.cpu() - - tar_signal = tar_waves[:,:,trim:-trim].transpose(0,1).reshape(2, -1).numpy()[:, :-pad] - - start = 0 if mix == 0 else margin_size - end = None if mix == list(mixes.keys())[::-1][0] else -margin_size - if margin_size == 0: - end = None - sources.append(tar_signal[:,start:end]) - - chunked_sources.append(sources) - _sources = np.concatenate(chunked_sources, axis=-1) - del self.onnx_models - - if onnxitera == 1: - widget_text.write('Done!\n') - else: - widget_text.write('\n') - - return _sources - - def demix_demucs(self, mix, margin_size): - processed = {} - demucsitera = len(mix) - demucsitera_calc = demucsitera * 2 - gui_progress_bar_demucs = 0 - progress_bar = 0 - if demucsitera == 1: - widget_text.write(base_text + f"Running Demucs Inference... ") - else: - widget_text.write(base_text + f"Running Demucs Inference...{space}\n") - - print(' Running Demucs Inference...') - for nmix in mix: - gui_progress_bar_demucs += 1 - progress_bar += 100 - step = (progress_bar / demucsitera) - if demucsitera == 1: - pass - else: - percent_prog = f"{base_text}Demucs Inference Progress: {gui_progress_bar_demucs}/{demucsitera} | {round(step)}%" - widget_text.percentage(percent_prog) - update_progress(**progress_kwargs, - step=(0.35 + (1.05/demucsitera_calc * gui_progress_bar_demucs))) - cmix = mix[nmix] - cmix = torch.tensor(cmix, dtype=torch.float32) - ref = cmix.mean(0) - cmix = (cmix - ref.mean()) / ref.std() - with torch.no_grad(): - sources = apply_model(self.demucs, cmix[None], - gui_progress_bar, - widget_text, - update_prog, - split=split_mode, - device=device, - overlap=overlap_set, - shifts=shift_set, - progress=False, - segmen=False, - **progress_demucs_kwargs)[0] - sources = (sources * ref.std() + ref.mean()).cpu().numpy() - sources[[0,1]] = sources[[1,0]] - - start = 0 if nmix == 0 else margin_size - end = None if nmix == list(mix.keys())[::-1][0] else -margin_size - if margin_size == 0: - end = None - processed[nmix] = sources[:,:,start:end].copy() - - sources = list(processed.values()) - sources = np.concatenate(sources, axis=-1) - - if demucsitera == 1: - widget_text.write('Done!\n') - else: - widget_text.write('\n') - #print('the demucs model is done running') - - return sources - - def demix_demucs_split(self, mix): - - if split_mode: - widget_text.write(base_text + f"Running Demucs Inference...{space}\n") - else: - widget_text.write(base_text + f"Running Demucs Inference... ") - print(' Running Demucs Inference...') - - mix = torch.tensor(mix, dtype=torch.float32) - ref = mix.mean(0) - mix = (mix - ref.mean()) / ref.std() - - with torch.no_grad(): - sources = apply_model(self.demucs, - mix[None], - gui_progress_bar, - widget_text, - update_prog, - split=split_mode, - device=device, - overlap=overlap_set, - shifts=shift_set, - progress=False, - segmen=True, - **progress_demucs_kwargs)[0] - - if split_mode: - widget_text.write('\n') - else: - widget_text.write('Done!\n') - - sources = (sources * ref.std() + ref.mean()).cpu().numpy() - sources[[0,1]] = sources[[1,0]] - - return sources - - def demix_demucs_v1(self, mix, margin_size): - processed = {} - demucsitera = len(mix) - demucsitera_calc = demucsitera * 2 - gui_progress_bar_demucs = 0 - progress_bar = 0 - print(' Running Demucs Inference...') - if demucsitera == 1: - widget_text.write(base_text + f"Running Demucs v1 Inference... ") - else: - widget_text.write(base_text + f"Running Demucs v1 Inference...{space}\n") - for nmix in mix: - gui_progress_bar_demucs += 1 - progress_bar += 100 - step = (progress_bar / demucsitera) - if demucsitera == 1: - pass - else: - percent_prog = f"{base_text}Demucs v1 Inference Progress: {gui_progress_bar_demucs}/{demucsitera} | {round(step)}%" - widget_text.percentage(percent_prog) - update_progress(**progress_kwargs, - step=(0.35 + (1.05/demucsitera_calc * gui_progress_bar_demucs))) - cmix = mix[nmix] - cmix = torch.tensor(cmix, dtype=torch.float32) - ref = cmix.mean(0) - cmix = (cmix - ref.mean()) / ref.std() - with torch.no_grad(): - sources = apply_model_v1(self.demucs, - cmix.to(device), - gui_progress_bar, - widget_text, - update_prog, - split=split_mode, - segmen=False, - shifts=shift_set, - **progress_demucs_kwargs) - sources = (sources * ref.std() + ref.mean()).cpu().numpy() - sources[[0,1]] = sources[[1,0]] - - start = 0 if nmix == 0 else margin_size - end = None if nmix == list(mix.keys())[::-1][0] else -margin_size - if margin_size == 0: - end = None - processed[nmix] = sources[:,:,start:end].copy() - - sources = list(processed.values()) - sources = np.concatenate(sources, axis=-1) - - if demucsitera == 1: - widget_text.write('Done!\n') - else: - widget_text.write('\n') - - return sources - - def demix_demucs_v1_split(self, mix): - - print(' Running Demucs Inference...') - if split_mode: - widget_text.write(base_text + f"Running Demucs v1 Inference...{space}\n") - else: - widget_text.write(base_text + f"Running Demucs v1 Inference... ") - - mix = torch.tensor(mix, dtype=torch.float32) - ref = mix.mean(0) - mix = (mix - ref.mean()) / ref.std() - - with torch.no_grad(): - sources = apply_model_v1(self.demucs, - mix.to(device), - gui_progress_bar, - widget_text, - update_prog, - split=split_mode, - segmen=True, - shifts=shift_set, - **progress_demucs_kwargs) - sources = (sources * ref.std() + ref.mean()).cpu().numpy() - sources[[0,1]] = sources[[1,0]] - - if split_mode: - widget_text.write('\n') - else: - widget_text.write('Done!\n') - - return sources - - def demix_demucs_v2(self, mix, margin_size): - processed = {} - demucsitera = len(mix) - demucsitera_calc = demucsitera * 2 - gui_progress_bar_demucs = 0 - progress_bar = 0 - if demucsitera == 1: - widget_text.write(base_text + f"Running Demucs v2 Inference... ") - else: - widget_text.write(base_text + f"Running Demucs v2 Inference...{space}\n") - - for nmix in mix: - gui_progress_bar_demucs += 1 - progress_bar += 100 - step = (progress_bar / demucsitera) - if demucsitera == 1: - pass - else: - percent_prog = f"{base_text}Demucs v2 Inference Progress: {gui_progress_bar_demucs}/{demucsitera} | {round(step)}%" - widget_text.percentage(percent_prog) - - update_progress(**progress_kwargs, - step=(0.35 + (1.05/demucsitera_calc * gui_progress_bar_demucs))) - cmix = mix[nmix] - cmix = torch.tensor(cmix, dtype=torch.float32) - ref = cmix.mean(0) - cmix = (cmix - ref.mean()) / ref.std() - with torch.no_grad(): - sources = apply_model_v2(self.demucs, - cmix.to(device), - gui_progress_bar, - widget_text, - update_prog, - split=split_mode, - segmen=False, - overlap=overlap_set, - shifts=shift_set, - **progress_demucs_kwargs) - sources = (sources * ref.std() + ref.mean()).cpu().numpy() - sources[[0,1]] = sources[[1,0]] - - start = 0 if nmix == 0 else margin_size - end = None if nmix == list(mix.keys())[::-1][0] else -margin_size - if margin_size == 0: - end = None - processed[nmix] = sources[:,:,start:end].copy() - - sources = list(processed.values()) - sources = np.concatenate(sources, axis=-1) - - if demucsitera == 1: - widget_text.write('Done!\n') - else: - widget_text.write('\n') - - return sources - - def demix_demucs_v2_split(self, mix): - print(' Running Demucs Inference...') - - if split_mode: - widget_text.write(base_text + f"Running Demucs v2 Inference...{space}\n") - else: - widget_text.write(base_text + f"Running Demucs v2 Inference... ") - - mix = torch.tensor(mix, dtype=torch.float32) - ref = mix.mean(0) - mix = (mix - ref.mean()) / ref.std() - with torch.no_grad(): - sources = apply_model_v2(self.demucs, - mix.to(device), - gui_progress_bar, - widget_text, - update_prog, - split=split_mode, - segmen=True, - overlap=overlap_set, - shifts=shift_set, - **progress_demucs_kwargs) - - sources = (sources * ref.std() + ref.mean()).cpu().numpy() - sources[[0,1]] = sources[[1,0]] - - if split_mode: - widget_text.write('\n') - else: - widget_text.write('Done!\n') - - return sources - -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 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', - 'no_chunk': False, - 'noise_pro_select': 'Auto Select', - 'noisereduc_s': 3, - 'non_red': False, - 'normalize': False, - 'output_image': True, - 'overlap': 0.5, - 'postprocess': True, - 'saveFormat': 'wav', - 'segment': 'Default', - '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""" - - total_count = model_count * total_files - base = (100 / total_count) - progress = base * current_model_bar - base - 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, - stop_thread, - **kwargs: dict): - - global widget_text - global gui_progress_bar - global music_file - global default_chunks - global default_noisereduc_s - global gui_progress_bar - 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 progress_demucs_kwargs - 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 no_chunk_demucs - global wav_type_set - global flac_type_set - global mp3_bit_set - global model_hash - global space - global stime - global stemset_n - global source_val - global widget_button - global stop_button - - wav_type_set = data['wavtype'] - - # Update default settings - default_chunks = data['chunks'] - default_noisereduc_s = data['noisereduc_s'] - autocompensate = data['autocompensate'] - - stop_button = stop_thread - widget_text = text_widget - gui_progress_bar = progress_var - widget_button = button_widget - space = ' '*90 - - #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) - - global update_prog - - update_prog = update_progress - no_chunk_demucs = data['no_chunk'] - space = ' '*90 - - 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'] - - basic_vr_ensemble_list = [vr_ensem_a_name, vr_ensem_b_name, vr_ensem_c_name, vr_ensem_d_name, vr_ensem_e_name] - no_models = basic_vr_ensemble_list.count('No Model') - vr_ensem_count = 5 - no_models - - 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' - elif mdx_net_model_name == 'UVR-MDX-NET Inst 1': - mdx_ensem = 'UVR_MDXNET_Inst_1' - elif mdx_net_model_name == 'UVR-MDX-NET Inst 2': - mdx_ensem = 'UVR_MDXNET_Inst_2' - 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' - elif mdx_net_model_name == 'UVR-MDX-NET Inst 1': - mdx_ensem_b = 'UVR_MDXNET_Inst_1' - elif mdx_net_model_name == 'UVR-MDX-NET Inst 2': - mdx_ensem_b = 'UVR_MDXNET_Inst_2' - - 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' - - multi_ai_ensemble_list = [vr_ensem_name, vr_ensem_mdx_a_name, vr_ensem_mdx_b_name, vr_ensem_mdx_c_name, data['mdx_ensem'], data['mdx_ensem_b']] - no_multi_models = multi_ai_ensemble_list.count('No Model') - multi_ensem_count = 6 - no_multi_models - - 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' - elif mdx_net_model_name == 'UVR-MDX-NET Inst 1': - mdx_only_ensem_a = 'UVR_MDXNET_Inst_1' - elif mdx_net_model_name == 'UVR-MDX-NET Inst 2': - mdx_only_ensem_a = 'UVR_MDXNET_Inst_2' - 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' - elif mdx_net_model_name == 'UVR-MDX-NET Inst 1': - mdx_only_ensem_b = 'UVR_MDXNET_Inst_1' - elif mdx_net_model_name == 'UVR-MDX-NET Inst 2': - mdx_only_ensem_b = 'UVR_MDXNET_Inst_2' - 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' - elif mdx_net_model_name == 'UVR-MDX-NET Inst 1': - mdx_only_ensem_c = 'UVR_MDXNET_Inst_1' - elif mdx_net_model_name == 'UVR-MDX-NET Inst 2': - mdx_only_ensem_c = 'UVR_MDXNET_Inst_2' - 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' - elif mdx_net_model_name == 'UVR-MDX-NET Inst 1': - mdx_only_ensem_d = 'UVR_MDXNET_Inst_1' - elif mdx_net_model_name == 'UVR-MDX-NET Inst 2': - mdx_only_ensem_d = 'UVR_MDXNET_Inst_2' - 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' - elif mdx_net_model_name == 'UVR-MDX-NET Inst 1': - mdx_only_ensem_e = 'UVR_MDXNET_Inst_1' - elif mdx_net_model_name == 'UVR-MDX-NET Inst 2': - mdx_only_ensem_e = 'UVR_MDXNET_Inst_2' - 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}' - } - ] - - basic_md_ensemble_list = [data['mdx_only_ensem_a'], data['mdx_only_ensem_b'], data['mdx_only_ensem_c'], data['mdx_only_ensem_d'], data['mdx_only_ensem_e']] - no_basic_md_models = basic_md_ensemble_list.count('No Model') - basic_md_ensem_count = 5 - no_basic_md_models - - global model_count - - if data['ensChoose'] == 'Multi-AI Ensemble': - loops = mdx_vr - ensefolder = 'Multi_AI_Ensemble_Outputs' - ensemode = 'Multi_AI_Ensemble' - model_count = multi_ensem_count - - if data['ensChoose'] == 'Basic VR Ensemble': - loops = Basic_Ensem - ensefolder = 'Basic_VR_Outputs' - ensemode = 'Multi_VR_Ensemble' - model_count = vr_ensem_count - - if data['ensChoose'] == 'Basic MD Ensemble': - loops = mdx_demuc_only - ensefolder = 'Basic_MDX_Net_Demucs_Ensemble' - ensemode = 'Basic_MDX_Net_Demucs_Ensemble' - model_count = basic_md_ensem_count - - global current_model_bar - - current_model_bar = 0 - - #Prepare Audiofile(s) - for file_num, music_file in enumerate(data['input_paths'], start=1): - # -Get text and update progress- - - current_model = 1 - - - 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} - - 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: - model_name = c['model_name'] - text_widget.write(f'Ensemble Mode - {model_name} - Model {current_model}/{model_count}\n\n') - current_model += 1 - current_model_bar += 1 - update_progress(**progress_kwargs, - step=0) - presentmodel = Path(c['model_location']) - - if presentmodel.is_file(): - pass - 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(base_text + 'Loading VR model... ') - - 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 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') - - 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, tta=False): - model.eval() - - global active_iterations - global progress_value - - with torch.no_grad(): - preds = [] - - iterations = [n_window] - - if data['tta']: - total_iterations = sum(iterations) - total_iterations = total_iterations*2 - else: - total_iterations = sum(iterations) - - if tta: - active_iterations = sum(iterations) - active_iterations = active_iterations - 2 - total_iterations = total_iterations - 2 - else: - active_iterations = 0 - - progress_bar = 0 - for i in range(n_window): - active_iterations += 1 - if data['demucsmodelVR']: - update_progress(**progress_kwargs, - step=(0.1 + (0.5/total_iterations * active_iterations))) - else: - update_progress(**progress_kwargs, - step=(0.1 + (0.8/total_iterations * active_iterations))) - start = i * roi_size - progress_bar += 100 - progress_value = progress_bar - active_iterations_step = active_iterations*100 - step = (active_iterations_step / total_iterations) - - percent_prog = f"{base_text}Inference Progress: {active_iterations}/{total_iterations} | {round(step)}%" - text_widget.percentage(percent_prog) - 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) - 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, tta=True) - 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 + f"Running Inferences (TTA)... {space}\n") - else: - text_widget.write(base_text + f"Running Inference... {space}\n") - - pred, X_mag, X_phase = inference(X_spec_m, - device, - model, aggressiveness) - - text_widget.write('\n') - - - 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(f'Ensemble Mode - {post_mdx_name} - Model {current_model}/{model_count}\n\n') - #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 demucs_only == 'on': - inference_type = 'demucs_only' - else: - inference_type = 'inference_mdx' - - progress_demucs_kwargs = {'total_files': len(data['input_paths']), - 'file_num': file_num, 'inference_type': inference_type} - - if data['noise_pro_select'] == 'Auto Select': - noise_pro_set = noise_pro - else: - noise_pro_set = data['noise_pro_select'] - - 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"]) - - current_model += 1 - current_model_bar += 1 - - update_progress(**progress_kwargs, - step=0) - - 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 stemset_n == '(Instrumental)': - if not 'UVR' in demucs_model_set: - if data['demucsmodel']: - widget_text.write(base_text + 'The selected Demucs model cannot be used with this model.\n') - widget_text.write(base_text + 'Only 2 stem Demucs models are compatible with this model.\n') - widget_text.write(base_text + 'Setting Demucs model to \"UVR_Demucs_Model_1\".\n\n') - demucs_model_set = 'UVR_Demucs_Model_1' - 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"\nGo to the Settings Menu and click \"Open Error Log\" for raw error details.\n") - text_widget.write(f'\nIf the error persists, please contact the developers.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while processing "{os.path.basename(music_file)}":\n' + - f'Process Method: 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("\nGo to the Settings Menu and click \"Open Error Log\" for raw error details.\n") - text_widget.write("\n" + f'Please address the error and try again.' + "\n") - text_widget.write(f'If this error persists, please contact the developers with the error details.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - 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