From e9c963d05d3417f6158ed65b32408bec3225caa2 Mon Sep 17 00:00:00 2001 From: Anjok07 <68268275+Anjok07@users.noreply.github.com> Date: Wed, 2 Nov 2022 03:25:01 -0500 Subject: [PATCH] Delete inference_MDX.py --- inference_MDX.py | 2280 ---------------------------------------------- 1 file changed, 2280 deletions(-) delete mode 100644 inference_MDX.py diff --git a/inference_MDX.py b/inference_MDX.py deleted file mode 100644 index 79c736c..0000000 --- a/inference_MDX.py +++ /dev/null @@ -1,2280 +0,0 @@ -from datetime import datetime -from demucs.apply import BagOfModels, apply_model -from demucs.hdemucs import HDemucs -from demucs.model_v2 import Demucs -from demucs.pretrained import get_model as _gm -from demucs.tasnet_v2 import ConvTasNet -from demucs.utils import apply_model_v1 -from demucs.utils import apply_model_v2 -from lib_v5 import spec_utils -from lib_v5.model_param_init import ModelParameters -from models import get_models, spec_effects -from pathlib import Path -from random import randrange -from tqdm import tqdm -from unittest import skip -import tkinter.ttk as ttk -import tkinter.messagebox -import tkinter.filedialog -import tkinter.simpledialog -import tkinter.font -import tkinter as tk -from tkinter import * -from tkinter.tix import * -import json -import gzip -import hashlib -import librosa -import numpy as np -import onnxruntime as ort -import os -import os.path -import pathlib -import psutil -import pydub -import shutil -import soundfile as sf -import subprocess -from UVR import MainWindow -import sys -import time -import time # Timer -import tkinter as tk -import torch -import traceback # Error Message Recent Calls -import warnings -import lib_v5.filelist - -#from typing import Literal - -class Predictor(): - def __init__(self): - pass - - def mdx_options(self): - """ - Open Advanced MDX Options - """ - self.okVar = tk.IntVar() - self.n_fft_scale_set_var = tk.StringVar(value='6144') - self.dim_f_set_var = tk.StringVar(value='2048') - self.mdxnetModeltype_var = tk.StringVar(value='Vocals') - self.noise_pro_select_set_var = tk.StringVar(value='MDX-NET_Noise_Profile_14_kHz') - self.compensate_v_var = tk.StringVar(value=1.03597672895) - - mdx_model_set = Toplevel() - - mdx_model_set.geometry("490x515") - window_height = 490 - window_width = 515 - - mdx_model_set.title("Specify Parameters") - - mdx_model_set.resizable(False, False) # This code helps to disable windows from resizing - - mdx_model_set.attributes("-topmost", True) - - screen_width = mdx_model_set.winfo_screenwidth() - screen_height = mdx_model_set.winfo_screenheight() - - x_cordinate = int((screen_width/2) - (window_width/2)) - y_cordinate = int((screen_height/2) - (window_height/2)) - - mdx_model_set.geometry("{}x{}+{}+{}".format(window_width, window_height, x_cordinate, y_cordinate)) - - x = main_window.winfo_x() - y = main_window.winfo_y() - mdx_model_set.geometry("+%d+%d" %(x+50,y+150)) - mdx_model_set.wm_transient(main_window) - - # change title bar icon - mdx_model_set.iconbitmap('img\\UVR-Icon-v2.ico') - - mdx_model_set_window = ttk.Notebook(mdx_model_set) - - mdx_model_set_window.pack(expand = 1, fill ="both") - - mdx_model_set_window.grid_rowconfigure(0, weight=1) - mdx_model_set_window.grid_columnconfigure(0, weight=1) - - frame0=Frame(mdx_model_set_window,highlightbackground='red',highlightthicknes=0) - frame0.grid(row=0,column=0,padx=0,pady=0) - - #frame0.tkraise(frame0) - - space_small = ' '*20 - space_small_1 = ' '*10 - - l0=tk.Label(frame0, text=f'\n{space_small}Stem Type{space_small}', font=("Century Gothic", "9"), foreground='#13a4c9') - l0.grid(row=3,column=0,padx=0,pady=5) - - l0=ttk.OptionMenu(frame0, self.mdxnetModeltype_var, None, 'Vocals', 'Instrumental', 'Other', 'Bass', 'Drums') - l0.grid(row=4,column=0,padx=0,pady=5) - - l0=tk.Label(frame0, text='N_FFT Scale', font=("Century Gothic", "9"), foreground='#13a4c9') - l0.grid(row=5,column=0,padx=0,pady=5) - - l0=tk.Label(frame0, text=f'{space_small_1}(Manual Set){space_small_1}', font=("Century Gothic", "9"), foreground='#13a4c9') - l0.grid(row=5,column=1,padx=0,pady=5) - - self.options_n_fft_scale_Opt = l0=ttk.OptionMenu(frame0, self.n_fft_scale_set_var, None, '4096', '6144', '7680', '8192', '16384') - - self.options_n_fft_scale_Opt - l0.grid(row=6,column=0,padx=0,pady=5) - - self.options_n_fft_scale_Entry = l0=ttk.Entry(frame0, textvariable=self.n_fft_scale_set_var, justify='center') - - self.options_n_fft_scale_Entry - l0.grid(row=6,column=1,padx=0,pady=5) - - l0=tk.Label(frame0, text='Dim_f', font=("Century Gothic", "9"), foreground='#13a4c9') - l0.grid(row=7,column=0,padx=0,pady=5) - - l0=tk.Label(frame0, text='(Manual Set)', font=("Century Gothic", "9"), foreground='#13a4c9') - l0.grid(row=7,column=1,padx=0,pady=5) - - self.options_dim_f_Opt = l0=ttk.OptionMenu(frame0, self.dim_f_set_var, None, '2048', '3072', '4096') - - self.options_dim_f_Opt - l0.grid(row=8,column=0,padx=0,pady=5) - - self.options_dim_f_Entry = l0=ttk.Entry(frame0, textvariable=self.dim_f_set_var, justify='center') - - self.options_dim_f_Entry - l0.grid(row=8,column=1,padx=0,pady=5) - - l0=tk.Label(frame0, text='Noise Profile', font=("Century Gothic", "9"), foreground='#13a4c9') - l0.grid(row=9,column=0,padx=0,pady=5) - - l0=ttk.OptionMenu(frame0, self.noise_pro_select_set_var, None, 'MDX-NET_Noise_Profile_14_kHz', 'MDX-NET_Noise_Profile_17_kHz', 'MDX-NET_Noise_Profile_Full_Band') - l0.grid(row=10,column=0,padx=0,pady=5) - - l0=tk.Label(frame0, text='Volume Compensation', font=("Century Gothic", "9"), foreground='#13a4c9') - l0.grid(row=11,column=0,padx=0,pady=10) - - self.options_compensate = l0=ttk.Entry(frame0, textvariable=self.compensate_v_var, justify='center') - - self.options_compensate - l0.grid(row=12,column=0,padx=0,pady=0) - - l0=ttk.Button(frame0,text="Continue", command=lambda: self.okVar.set(1)) - l0.grid(row=13,column=0,padx=0,pady=30) - - def stop(): - widget_text.write(f'Please configure the ONNX model settings accordingly and try again.\n\n') - widget_text.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - torch.cuda.empty_cache() - gui_progress_bar.set(0) - widget_button.configure(state=tk.NORMAL) # Enable Button - self.okVar.set(1) - stop_button() - mdx_model_set.destroy() - return - - l0=ttk.Button(frame0,text="Stop Process", command=stop) - l0.grid(row=13,column=1,padx=0,pady=30) - - mdx_model_set.protocol("WM_DELETE_WINDOW", stop) - - frame0.wait_variable(self.okVar) - - global n_fft_scale_set - global dim_f_set - global modeltype - global stemset_n - global stem_text_a - global stem_text_b - global source_val - global noise_pro_set - global compensate - global demucs_model_set - - stemtype = self.mdxnetModeltype_var.get() - - if stemtype == 'Vocals': - modeltype = 'v' - stemset_n = '(Vocals)' - source_val = 3 - if stemtype == 'Instrumental': - modeltype = 'v' - stemset_n = '(Instrumental)' - source_val = 0 - if stemtype == 'Other': - modeltype = 'o' - stemset_n = '(Other)' - source_val = 2 - if stemtype == 'Drums': - modeltype = 'd' - stemset_n = '(Drums)' - source_val = 1 - if stemtype == 'Bass': - modeltype = 'b' - stemset_n = '(Bass)' - source_val = 0 - - if stemset_n == '(Vocals)': - stem_text_a = 'Vocals' - stem_text_b = 'Instrumental' - elif stemset_n == '(Instrumental)': - stem_text_a = 'Instrumental' - stem_text_b = 'Vocals' - elif stemset_n == '(Other)': - stem_text_a = 'Other' - stem_text_b = 'mixture without selected stem' - elif stemset_n == '(Drums)': - stem_text_a = 'Drums' - stem_text_b = 'mixture without selected stem' - elif stemset_n == '(Bass)': - stem_text_a = 'Bass' - stem_text_b = 'mixture without selected stem' - else: - stem_text_a = 'Vocals' - stem_text_b = 'Instrumental' - - compensate = self.compensate_v_var.get() - n_fft_scale_set = int(self.n_fft_scale_set_var.get()) - dim_f_set = int(self.dim_f_set_var.get()) - noise_pro_set = self.noise_pro_select_set_var.get() - - mdx_model_params = { - 'modeltype' : modeltype, - 'stemset_n' : stemset_n, - 'source_val' : source_val, - 'compensate' : compensate, - 'n_fft_scale_set' : n_fft_scale_set, - 'dim_f_set' : dim_f_set, - 'noise_pro' : noise_pro_set, - } - - mdx_model_params_r = json.dumps(mdx_model_params, indent=4) - - with open(f"lib_v5/filelists/model_cache/mdx_model_cache/{model_hash}.json", "w") as outfile: - outfile.write(mdx_model_params_r) - - if 'UVR' in demucs_model_set: - if stemset_n == '(Bass)' or stemset_n == '(Drums)' or stemset_n == '(Other)': - widget_text.write(base_text + 'The selected Demucs model can only be used with vocal or instrumental stems.\n') - widget_text.write(base_text + 'Please select a 4 stem Demucs model next time.\n') - widget_text.write(base_text + 'Setting Demucs Model to \"mdx_extra\"\n') - demucs_model_set = 'mdx_extra' - - - if stemset_n == '(Instrumental)': - if not 'UVR' in demucs_model_set: - widget_text.write(base_text + 'The selected Demucs model cannot be used with this model.\n') - widget_text.write(base_text + 'Only 2 stem Demucs models are compatible with this model.\n') - widget_text.write(base_text + 'Setting Demucs model to \"UVR_Demucs_Model_1\".\n\n') - demucs_model_set = 'UVR_Demucs_Model_1' - - mdx_model_set.destroy() - - def prediction_setup(self): - - global device - - if data['gpu'] >= 0: - device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') - if data['gpu'] == -1: - device = torch.device('cpu') - - if data['demucsmodel']: - if demucs_model_version == 'v1': - load_from = "models/Demucs_Models/"f"{demucs_model_set}" - if str(load_from).endswith(".gz"): - load_from = gzip.open(load_from, "rb") - klass, args, kwargs, state = torch.load(load_from) - self.demucs = klass(*args, **kwargs) - widget_text.write(base_text + 'Loading Demucs v1 model... ') - update_progress(**progress_kwargs, - step=0.05) - self.demucs.to(device) - self.demucs.load_state_dict(state) - widget_text.write('Done!\n') - if not data['segment'] == 'Default': - widget_text.write(base_text + 'Segments is only available in Demucs v3. Please use \"Chunks\" instead.\n') - else: - pass - - if demucs_model_version == 'v2': - if '48' in demucs_model_set: - channels=48 - elif 'unittest' in demucs_model_set: - channels=4 - else: - channels=64 - - if 'tasnet' in demucs_model_set: - self.demucs = ConvTasNet(sources=["drums", "bass", "other", "vocals"], X=10) - else: - self.demucs = Demucs(sources=["drums", "bass", "other", "vocals"], channels=channels) - widget_text.write(base_text + 'Loading Demucs v2 model... ') - update_progress(**progress_kwargs, - step=0.05) - self.demucs.to(device) - self.demucs.load_state_dict(torch.load("models/Demucs_Models/"f"{demucs_model_set}")) - widget_text.write('Done!\n') - if not data['segment'] == 'Default': - widget_text.write(base_text + 'Segments is only available in Demucs v3. Please use \"Chunks\" instead.\n') - else: - pass - self.demucs.eval() - - if demucs_model_version == 'v3': - if 'UVR' in demucs_model_set: - self.demucs = HDemucs(sources=["other", "vocals"]) - else: - self.demucs = HDemucs(sources=["drums", "bass", "other", "vocals"]) - widget_text.write(base_text + 'Loading Demucs model... ') - update_progress(**progress_kwargs, - step=0.05) - path_d = Path('models/Demucs_Models/v3_repo') - #print('What Demucs model was chosen? ', demucs_model_set) - self.demucs = _gm(name=demucs_model_set, repo=path_d) - self.demucs.to(device) - self.demucs.eval() - widget_text.write('Done!\n') - if isinstance(self.demucs, BagOfModels): - widget_text.write(base_text + f"Selected Demucs model is a bag of {len(self.demucs.models)} model(s).\n") - - if data['segment'] == 'Default': - segment = None - if isinstance(self.demucs, BagOfModels): - if segment is not None: - for sub in self.demucs.models: - sub.segment = segment - else: - if segment is not None: - sub.segment = segment - else: - try: - segment = int(data['segment']) - if isinstance(self.demucs, BagOfModels): - if segment is not None: - for sub in self.demucs.models: - sub.segment = segment - else: - if segment is not None: - sub.segment = segment - if split_mode: - widget_text.write(base_text + "Segments set to "f"{segment}.\n") - except: - segment = None - if isinstance(self.demucs, BagOfModels): - if segment is not None: - for sub in self.demucs.models: - sub.segment = segment - else: - if segment is not None: - sub.segment = segment - - self.onnx_models = {} - c = 0 - - self.models = get_models('tdf_extra', load=False, device=cpu, stems=modeltype, n_fft_scale=int(n_fft_scale_set), dim_f=int(dim_f_set)) - if not data['demucs_only']: - widget_text.write(base_text + 'Loading ONNX model... ') - - update_progress(**progress_kwargs, - step=0.1) - c+=1 - - if data['gpu'] >= 0: - if torch.cuda.is_available(): - run_type = ['CUDAExecutionProvider'] - else: - data['gpu'] = -1 - widget_text.write("\n" + base_text + "No NVIDIA GPU detected. Switching to CPU... ") - run_type = ['CPUExecutionProvider'] - elif data['gpu'] == -1: - run_type = ['CPUExecutionProvider'] - - print('Selected Model: ', mdx_model_path) - self.onnx_models[c] = ort.InferenceSession(os.path.join(mdx_model_path), providers=run_type) - - if not data['demucs_only']: - widget_text.write('Done!\n') - - def prediction(self, m): - mix, samplerate = librosa.load(m, mono=False, sr=44100) - #print('print mix: ', mix) - if mix.ndim == 1: - mix = np.asfortranarray([mix,mix]) - samplerate = samplerate - mix = mix.T - sources = self.demix(mix.T) - widget_text.write(base_text + 'Inferences complete!\n') - c = -1 - - #Main Save Path - save_path = os.path.dirname(_basename) - - inst_only = data['inst_only'] - voc_only = data['voc_only'] - - #print('stemset_n: ', stemset_n) - - if stemset_n == '(Instrumental)': - if data['inst_only'] == True: - voc_only = True - inst_only = False - if data['voc_only'] == True: - inst_only = True - voc_only = False - - #Vocal Path - if stemset_n == '(Vocals)': - vocal_name = '(Vocals)' - elif stemset_n == '(Instrumental)': - vocal_name = '(Instrumental)' - elif stemset_n == '(Other)': - vocal_name = '(Other)' - elif stemset_n == '(Drums)': - vocal_name = '(Drums)' - elif stemset_n == '(Bass)': - vocal_name = '(Bass)' - - if data['modelFolder']: - vocal_path = '{save_path}/{file_name}.wav'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{vocal_name}_{mdx_model_name}',) - vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{vocal_name}_{mdx_model_name}',) - vocal_path_flac = '{save_path}/{file_name}.flac'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{vocal_name}_{mdx_model_name}',) - else: - vocal_path = '{save_path}/{file_name}.wav'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{vocal_name}',) - vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{vocal_name}',) - vocal_path_flac = '{save_path}/{file_name}.flac'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{vocal_name}',) - - #Instrumental Path - - if stemset_n == '(Vocals)': - Instrumental_name = '(Instrumental)' - elif stemset_n == '(Instrumental)': - Instrumental_name = '(Vocals)' - elif stemset_n == '(Other)': - Instrumental_name = '(No_Other)' - elif stemset_n == '(Drums)': - Instrumental_name = '(No_Drums)' - elif stemset_n == '(Bass)': - Instrumental_name = '(No_Bass)' - - if data['modelFolder']: - Instrumental_path = '{save_path}/{file_name}.wav'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{mdx_model_name}',) - Instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{mdx_model_name}',) - Instrumental_path_flac = '{save_path}/{file_name}.flac'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{mdx_model_name}',) - else: - Instrumental_path = '{save_path}/{file_name}.wav'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{Instrumental_name}',) - Instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{Instrumental_name}',) - Instrumental_path_flac = '{save_path}/{file_name}.flac'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{Instrumental_name}',) - - #Non-Reduced Vocal Path - if stemset_n == '(Vocals)': - vocal_name = '(Vocals)' - elif stemset_n == '(Other)': - vocal_name = '(Other)' - elif stemset_n == '(Drums)': - vocal_name = '(Drums)' - elif stemset_n == '(Bass)': - vocal_name = '(Bass)' - elif stemset_n == '(Instrumental)': - vocal_name = '(Instrumental)' - - if data['modelFolder']: - non_reduced_vocal_path = '{save_path}/{file_name}.wav'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{vocal_name}_{mdx_model_name}_No_Reduction',) - non_reduced_vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{vocal_name}_{mdx_model_name}_No_Reduction',) - non_reduced_vocal_path_flac = '{save_path}/{file_name}.flac'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{vocal_name}_{mdx_model_name}_No_Reduction',) - else: - non_reduced_vocal_path = '{save_path}/{file_name}.wav'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{vocal_name}_No_Reduction',) - non_reduced_vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{vocal_name}_No_Reduction',) - non_reduced_vocal_path_flac = '{save_path}/{file_name}.flac'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{vocal_name}_No_Reduction',) - - if data['modelFolder']: - non_reduced_Instrumental_path = '{save_path}/{file_name}.wav'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{mdx_model_name}_No_Reduction',) - non_reduced_Instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{mdx_model_name}_No_Reduction',) - non_reduced_Instrumental_path_flac = '{save_path}/{file_name}.flac'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{mdx_model_name}_No_Reduction',) - else: - non_reduced_Instrumental_path = '{save_path}/{file_name}.wav'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_No_Reduction',) - non_reduced_Instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_No_Reduction',) - non_reduced_Instrumental_path_flac = '{save_path}/{file_name}.flac'.format( - save_path=save_path, - file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_No_Reduction',) - - - if os.path.isfile(non_reduced_vocal_path): - file_exists_n = 'there' - else: - file_exists_n = 'not_there' - - if os.path.isfile(vocal_path): - file_exists_v = 'there' - else: - file_exists_v = 'not_there' - - if os.path.isfile(Instrumental_path): - file_exists_i = 'there' - else: - file_exists_i = 'not_there' - - #print('Is there already a voc file there? ', file_exists_v) - - if not data['noisereduc_s'] == 'None': - c += 1 - - if not data['demucsmodel']: - if inst_only: - widget_text.write(base_text + f'Preparing to save {stem_text_b}...') - else: - widget_text.write(base_text + f'Saving {stem_text_a}... ') - - sf.write(non_reduced_vocal_path, sources[c].T, samplerate, subtype=wav_type_set) - update_progress(**progress_kwargs, - step=(0.9)) - widget_text.write('Done!\n') - widget_text.write(base_text + 'Performing Noise Reduction... ') - reduction_sen = float(int(data['noisereduc_s'])/10) - subprocess.call("lib_v5\\sox\\sox.exe" + ' "' + - f"{str(non_reduced_vocal_path)}" + '" "' + f"{str(vocal_path)}" + '" ' + - "noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}", - shell=True, stdout=subprocess.PIPE, - stdin=subprocess.PIPE, stderr=subprocess.PIPE) - widget_text.write('Done!\n') - update_progress(**progress_kwargs, - step=(0.95)) - else: - if inst_only: - widget_text.write(base_text + f'Preparing {stem_text_b}...') - else: - widget_text.write(base_text + f'Saving {stem_text_a}... ') - - if data['demucs_only']: - if 'UVR' in demucs_model_set: - if stemset_n == '(Instrumental)': - sf.write(non_reduced_vocal_path, sources[0].T, samplerate, subtype=wav_type_set) - else: - sf.write(non_reduced_vocal_path, sources[1].T, samplerate, subtype=wav_type_set) - else: - sf.write(non_reduced_vocal_path, sources[source_val].T, samplerate, subtype=wav_type_set) - update_progress(**progress_kwargs, - step=(0.9)) - widget_text.write('Done!\n') - widget_text.write(base_text + 'Performing Noise Reduction... ') - reduction_sen = float(data['noisereduc_s'])/10 - #print(noise_pro_set) - subprocess.call("lib_v5\\sox\\sox.exe" + ' "' + - f"{str(non_reduced_vocal_path)}" + '" "' + f"{str(vocal_path)}" + '" ' + - "noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}", - shell=True, stdout=subprocess.PIPE, - stdin=subprocess.PIPE, stderr=subprocess.PIPE) - update_progress(**progress_kwargs, - step=(0.95)) - widget_text.write('Done!\n') - else: - c += 1 - - if not data['demucsmodel']: - if inst_only: - widget_text.write(base_text + f'Preparing {stem_text_b}...') - else: - widget_text.write(base_text + f'Saving {stem_text_a}... ') - sf.write(vocal_path, sources[c].T, samplerate, subtype=wav_type_set) - update_progress(**progress_kwargs, - step=(0.9)) - widget_text.write('Done!\n') - else: - if inst_only: - widget_text.write(base_text + f'Preparing {stem_text_b}...') - else: - widget_text.write(base_text + f'Saving {stem_text_a}... ') - - if data['demucs_only']: - if 'UVR' in demucs_model_set: - if stemset_n == '(Instrumental)': - sf.write(vocal_path, sources[0].T, samplerate, subtype=wav_type_set) - else: - sf.write(vocal_path, sources[1].T, samplerate, subtype=wav_type_set) - else: - sf.write(vocal_path, sources[source_val].T, samplerate, subtype=wav_type_set) - else: - sf.write(vocal_path, sources[source_val].T, samplerate, subtype=wav_type_set) - - update_progress(**progress_kwargs, - step=(0.9)) - widget_text.write('Done!\n') - - if voc_only and not inst_only: - pass - else: - if not data['noisereduc_s'] == 'None': - if data['nophaseinst']: - finalfiles = [ - { - 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', - 'files':[str(music_file), non_reduced_vocal_path], - } - ] - else: - finalfiles = [ - { - 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', - 'files':[str(music_file), vocal_path], - } - ] - else: - finalfiles = [ - { - 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', - 'files':[str(music_file), vocal_path], - } - ] - - widget_text.write(base_text + f'Saving {stem_text_b}... ') - for i, e in tqdm(enumerate(finalfiles)): - - wave, specs = {}, {} - - mp = ModelParameters(e['model_params']) - - for i in range(len(e['files'])): - spec = {} - - for d in range(len(mp.param['band']), 0, -1): - bp = mp.param['band'][d] - - if d == len(mp.param['band']): # high-end band - wave[d], _ = librosa.load( - e['files'][i], bp['sr'], False, dtype=np.float32, res_type=bp['res_type']) - - if len(wave[d].shape) == 1: # mono to stereo - wave[d] = np.array([wave[d], wave[d]]) - else: # lower bands - wave[d] = librosa.resample(wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type']) - - spec[d] = spec_utils.wave_to_spectrogram(wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']) - - specs[i] = spec_utils.combine_spectrograms(spec, mp) - - del wave - - ln = min([specs[0].shape[2], specs[1].shape[2]]) - specs[0] = specs[0][:,:,:ln] - specs[1] = specs[1][:,:,:ln] - X_mag = np.abs(specs[0]) - y_mag = np.abs(specs[1]) - max_mag = np.where(X_mag >= y_mag, X_mag, y_mag) - v_spec = specs[1] - max_mag * np.exp(1.j * np.angle(specs[0])) - - update_progress(**progress_kwargs, - step=(1)) - - if not data['noisereduc_s'] == 'None': - if data['nophaseinst']: - sf.write(non_reduced_Instrumental_path, normalization_set(spec_utils.cmb_spectrogram_to_wave(-v_spec, mp)), mp.param['sr'], subtype=wav_type_set) - - reduction_sen = float(data['noisereduc_s'])/10 - #print(noise_pro_set) - - subprocess.call("lib_v5\\sox\\sox.exe" + ' "' + - f"{str(non_reduced_Instrumental_path)}" + '" "' + f"{str(Instrumental_path)}" + '" ' + - "noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}", - shell=True, stdout=subprocess.PIPE, - stdin=subprocess.PIPE, stderr=subprocess.PIPE) - else: - sf.write(Instrumental_path, normalization_set(spec_utils.cmb_spectrogram_to_wave(-v_spec, mp)), mp.param['sr'], subtype=wav_type_set) - else: - sf.write(Instrumental_path, normalization_set(spec_utils.cmb_spectrogram_to_wave(-v_spec, mp)), mp.param['sr'], subtype=wav_type_set) - - if inst_only: - if file_exists_v == 'there': - pass - else: - try: - os.remove(vocal_path) - except: - pass - - widget_text.write('Done!\n') - - if data['saveFormat'] == 'Mp3': - try: - - if inst_only == True: - if data['non_red'] == True: - if not data['nophaseinst']: - pass - else: - musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path) - musfile.export(non_reduced_Instrumental_path_mp3, format="mp3", bitrate=mp3_bit_set) - try: - os.remove(non_reduced_Instrumental_path) - except: - pass - pass - else: - musfile = pydub.AudioSegment.from_wav(vocal_path) - musfile.export(vocal_path_mp3, format="mp3", bitrate=mp3_bit_set) - if file_exists_v == 'there': - pass - else: - try: - os.remove(vocal_path) - except: - pass - if data['non_red'] == True: - if not data['nophaseinst']: - pass - else: - if voc_only == True: - pass - else: - musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path) - musfile.export(non_reduced_Instrumental_path_mp3, format="mp3", bitrate=mp3_bit_set) - if file_exists_n == 'there': - pass - else: - try: - os.remove(non_reduced_Instrumental_path) - except: - pass - if voc_only == True: - if data['non_red'] == True: - musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path) - musfile.export(non_reduced_vocal_path_mp3, format="mp3", bitrate=mp3_bit_set) - try: - os.remove(non_reduced_vocal_path) - except: - pass - pass - else: - musfile = pydub.AudioSegment.from_wav(Instrumental_path) - musfile.export(Instrumental_path_mp3, format="mp3", bitrate=mp3_bit_set) - if file_exists_i == 'there': - pass - else: - try: - os.remove(Instrumental_path) - except: - pass - if data['non_red'] == True: - if inst_only == True: - pass - else: - musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path) - musfile.export(non_reduced_vocal_path_mp3, format="mp3", bitrate=mp3_bit_set) - if file_exists_n == 'there': - pass - else: - try: - os.remove(non_reduced_vocal_path) - except: - pass - - except Exception as e: - traceback_text = ''.join(traceback.format_tb(e.__traceback__)) - errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' - if "ffmpeg" in errmessage: - widget_text.write(base_text + 'Failed to save output(s) as Mp3(s).\n') - widget_text.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') - widget_text.write(base_text + 'Moving on...\n') - else: - widget_text.write(base_text + 'Failed to save output(s) as Mp3(s).\n') - widget_text.write(base_text + 'Please check error log.\n') - widget_text.write(base_text + 'Moving on...\n') - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while attempting to save file as mp3 "{os.path.basename(music_file)}":\n\n' + - f'Process Method: MDX-Net\n\n' + - f'FFmpeg might be missing or corrupted.\n\n' + - f'If this error persists, please contact the developers.\n\n' + - f'Raw error details:\n\n' + - errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - pass - - if data['saveFormat'] == 'Flac': - try: - if inst_only == True: - if data['non_red'] == True: - if not data['nophaseinst']: - pass - else: - musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path) - musfile.export(non_reduced_Instrumental_path_flac, format="flac") - try: - os.remove(non_reduced_Instrumental_path) - except: - pass - pass - else: - musfile = pydub.AudioSegment.from_wav(vocal_path) - musfile.export(vocal_path_flac, format="flac") - if file_exists_v == 'there': - pass - else: - try: - os.remove(vocal_path) - except: - pass - if data['non_red'] == True: - if not data['nophaseinst']: - pass - else: - if voc_only == True: - pass - else: - musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path) - musfile.export(non_reduced_Instrumental_path_flac, format="flac") - if file_exists_n == 'there': - pass - else: - try: - os.remove(non_reduced_Instrumental_path) - except: - pass - if voc_only == True: - if data['non_red'] == True: - musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path) - musfile.export(non_reduced_vocal_path_flac, format="flac") - try: - os.remove(non_reduced_vocal_path) - except: - pass - pass - else: - musfile = pydub.AudioSegment.from_wav(Instrumental_path) - musfile.export(Instrumental_path_flac, format="flac") - if file_exists_i == 'there': - pass - else: - try: - os.remove(Instrumental_path) - except: - pass - if data['non_red'] == True: - if inst_only == True: - pass - else: - musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path) - musfile.export(non_reduced_vocal_path_flac, format="flac") - if file_exists_n == 'there': - pass - else: - try: - os.remove(non_reduced_vocal_path) - except: - pass - - except Exception as e: - traceback_text = ''.join(traceback.format_tb(e.__traceback__)) - errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' - if "ffmpeg" in errmessage: - widget_text.write(base_text + 'Failed to save output(s) as Flac(s).\n') - widget_text.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') - widget_text.write(base_text + 'Moving on...\n') - else: - widget_text.write(base_text + 'Failed to save output(s) as Flac(s).\n') - widget_text.write(base_text + 'Please check error log.\n') - widget_text.write(base_text + 'Moving on...\n') - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while attempting to save file as flac "{os.path.basename(music_file)}":\n\n' + - f'Process Method: MDX-Net\n\n' + - f'FFmpeg might be missing or corrupted.\n\n' + - f'If this error persists, please contact the developers.\n\n' + - f'Raw error details:\n\n' + - errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - pass - - if data['noisereduc_s'] == 'None': - pass - elif data['non_red'] == True: - if inst_only: - if file_exists_n == 'there': - pass - else: - try: - os.remove(non_reduced_vocal_path) - except: - pass - pass - elif inst_only: - if file_exists_n == 'there': - pass - else: - try: - os.remove(non_reduced_vocal_path) - except: - pass - else: - try: - os.remove(non_reduced_vocal_path) - os.remove(non_reduced_Instrumental_path) - except: - pass - - widget_text.write(base_text + 'Completed Separation!\n') - - def demix(self, mix): - global chunk_set - - # 1 = demucs only - # 0 = onnx only - if data['chunks'] == 'Full': - chunk_set = 0 - widget_text.write(base_text + "Chunk size user-set to \"Full\"... \n") - elif data['chunks'] == 'Auto': - if data['gpu'] == 0: - try: - gpu_mem = round(torch.cuda.get_device_properties(0).total_memory/1.074e+9) - except: - widget_text.write(base_text + 'NVIDIA GPU Required for conversion!\n') - data['gpu'] = -1 - pass - if int(gpu_mem) <= int(6): - chunk_set = int(5) - widget_text.write(base_text + 'Chunk size auto-set to 5... \n') - if gpu_mem in [7, 8, 9, 10, 11, 12, 13, 14, 15]: - chunk_set = int(10) - widget_text.write(base_text + 'Chunk size auto-set to 10... \n') - if int(gpu_mem) >= int(16): - chunk_set = int(40) - widget_text.write(base_text + 'Chunk size auto-set to 40... \n') - if data['gpu'] == -1: - sys_mem = psutil.virtual_memory().total >> 30 - if int(sys_mem) <= int(4): - chunk_set = int(1) - widget_text.write(base_text + 'Chunk size auto-set to 1... \n') - if sys_mem in [5, 6, 7, 8]: - chunk_set = int(10) - widget_text.write(base_text + 'Chunk size auto-set to 10... \n') - if sys_mem in [9, 10, 11, 12, 13, 14, 15, 16]: - chunk_set = int(25) - widget_text.write(base_text + 'Chunk size auto-set to 25... \n') - if int(sys_mem) >= int(17): - chunk_set = int(60) - widget_text.write(base_text + 'Chunk size auto-set to 60... \n') - elif data['chunks'] == '0': - chunk_set = 0 - widget_text.write(base_text + "Chunk size user-set to \"Full\"... \n") - else: - chunk_set = int(data['chunks']) - widget_text.write(base_text + "Chunk size user-set to "f"{chunk_set}... \n") - - samples = mix.shape[-1] - margin = margin_set - chunk_size = chunk_set*44100 - assert not margin == 0, 'margin cannot be zero!' - if margin > chunk_size: - margin = chunk_size - - b = np.array([[[0.5]], [[0.5]], [[0.7]], [[0.9]]]) - segmented_mix = {} - - if chunk_set == 0 or samples < chunk_size: - chunk_size = samples - - counter = -1 - for skip in range(0, samples, chunk_size): - counter+=1 - - s_margin = 0 if counter == 0 else margin - end = min(skip+chunk_size+margin, samples) - - start = skip-s_margin - - segmented_mix[skip] = mix[:,start:end].copy() - if end == samples: - break - - if not data['demucsmodel']: - sources = self.demix_base(segmented_mix, margin_size=margin) - elif data['demucs_only']: - if no_chunk_demucs == False: - sources = self.demix_demucs_split(mix) - if no_chunk_demucs == True: - sources = self.demix_demucs(segmented_mix, margin_size=margin) - else: # both, apply spec effects - base_out = self.demix_base(segmented_mix, margin_size=margin) - - if demucs_model_version == 'v1': - if no_chunk_demucs == False: - demucs_out = self.demix_demucs_v1_split(mix) - if no_chunk_demucs == True: - demucs_out = self.demix_demucs_v1(segmented_mix, margin_size=margin) - if demucs_model_version == 'v2': - if no_chunk_demucs == False: - demucs_out = self.demix_demucs_v2_split(mix) - if no_chunk_demucs == True: - demucs_out = self.demix_demucs_v2(segmented_mix, margin_size=margin) - if demucs_model_version == 'v3': - if no_chunk_demucs == False: - demucs_out = self.demix_demucs_split(mix) - if no_chunk_demucs == True: - demucs_out = self.demix_demucs(segmented_mix, margin_size=margin) - - nan_count = np.count_nonzero(np.isnan(demucs_out)) + np.count_nonzero(np.isnan(base_out)) - if nan_count > 0: - print('Warning: there are {} nan values in the array(s).'.format(nan_count)) - demucs_out, base_out = np.nan_to_num(demucs_out), np.nan_to_num(base_out) - sources = {} - #print(data['mixing']) - - if 'UVR' in demucs_model_set: - if stemset_n == '(Instrumental)': - sources[source_val] = (spec_effects(wave=[demucs_out[0],base_out[0]], - algorithm=data['mixing'], - value=b[source_val])*float(compensate)) # compensation - else: - sources[source_val] = (spec_effects(wave=[demucs_out[1],base_out[0]], - algorithm=data['mixing'], - value=b[source_val])*float(compensate)) # compensation - else: - sources[source_val] = (spec_effects(wave=[demucs_out[source_val],base_out[0]], - algorithm=data['mixing'], - value=b[source_val])*float(compensate)) # compensation - - if not data['demucsmodel']: - return sources*float(compensate) - else: - return sources - - def demix_base(self, mixes, margin_size): - chunked_sources = [] - onnxitera = len(mixes) - onnxitera_calc = onnxitera * 2 - gui_progress_bar_onnx = 0 - progress_bar = 0 - - print(' Running ONNX Inference...') - - if onnxitera == 1: - widget_text.write(base_text + f"Running ONNX Inference... ") - else: - widget_text.write(base_text + f"Running ONNX Inference...{space}\n") - - for mix in mixes: - gui_progress_bar_onnx += 1 - if data['demucsmodel']: - update_progress(**progress_kwargs, - step=(0.1 + (0.5/onnxitera_calc * gui_progress_bar_onnx))) - else: - update_progress(**progress_kwargs, - step=(0.1 + (0.9/onnxitera * gui_progress_bar_onnx))) - - progress_bar += 100 - step = (progress_bar / onnxitera) - - if onnxitera == 1: - pass - else: - percent_prog = f"{base_text}MDX-Net Inference Progress: {gui_progress_bar_onnx}/{onnxitera} | {round(step)}%" - widget_text.percentage(percent_prog) - - cmix = mixes[mix] - sources = [] - n_sample = cmix.shape[1] - - mod = 0 - for model in self.models: - mod += 1 - trim = model.n_fft//2 - gen_size = model.chunk_size-2*trim - pad = gen_size - n_sample%gen_size - mix_p = np.concatenate((np.zeros((2,trim)), cmix, np.zeros((2,pad)), np.zeros((2,trim))), 1) - mix_waves = [] - i = 0 - while i < n_sample + pad: - waves = np.array(mix_p[:, i:i+model.chunk_size]) - mix_waves.append(waves) - i += gen_size - mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu) - with torch.no_grad(): - _ort = self.onnx_models[mod] - spek = model.stft(mix_waves) - - tar_waves = model.istft(torch.tensor(_ort.run(None, {'input': spek.cpu().numpy()})[0]))#.cpu() - - tar_signal = tar_waves[:,:,trim:-trim].transpose(0,1).reshape(2, -1).numpy()[:, :-pad] - - start = 0 if mix == 0 else margin_size - end = None if mix == list(mixes.keys())[::-1][0] else -margin_size - if margin_size == 0: - end = None - sources.append(tar_signal[:,start:end]) - - chunked_sources.append(sources) - _sources = np.concatenate(chunked_sources, axis=-1) - del self.onnx_models - - if onnxitera == 1: - widget_text.write('Done!\n') - else: - widget_text.write('\n') - - return _sources - - def demix_demucs(self, mix, margin_size): - processed = {} - demucsitera = len(mix) - demucsitera_calc = demucsitera * 2 - gui_progress_bar_demucs = 0 - progress_bar = 0 - if demucsitera == 1: - widget_text.write(base_text + f"Running Demucs Inference... ") - else: - widget_text.write(base_text + f"Running Demucs Inference...{space}\n") - - print(' Running Demucs Inference...') - for nmix in mix: - gui_progress_bar_demucs += 1 - progress_bar += 100 - step = (progress_bar / demucsitera) - if demucsitera == 1: - pass - else: - percent_prog = f"{base_text}Demucs Inference Progress: {gui_progress_bar_demucs}/{demucsitera} | {round(step)}%" - widget_text.percentage(percent_prog) - update_progress(**progress_kwargs, - step=(0.35 + (1.05/demucsitera_calc * gui_progress_bar_demucs))) - cmix = mix[nmix] - cmix = torch.tensor(cmix, dtype=torch.float32) - ref = cmix.mean(0) - cmix = (cmix - ref.mean()) / ref.std() - with torch.no_grad(): - sources = apply_model(self.demucs, cmix[None], - gui_progress_bar, - widget_text, - update_prog, - split=split_mode, - device=device, - overlap=overlap_set, - shifts=shift_set, - progress=False, - segmen=False, - **progress_demucs_kwargs)[0] - sources = (sources * ref.std() + ref.mean()).cpu().numpy() - sources[[0,1]] = sources[[1,0]] - - start = 0 if nmix == 0 else margin_size - end = None if nmix == list(mix.keys())[::-1][0] else -margin_size - if margin_size == 0: - end = None - processed[nmix] = sources[:,:,start:end].copy() - - sources = list(processed.values()) - sources = np.concatenate(sources, axis=-1) - - if demucsitera == 1: - widget_text.write('Done!\n') - else: - widget_text.write('\n') - #print('the demucs model is done running') - - return sources - - def demix_demucs_split(self, mix): - - if split_mode: - widget_text.write(base_text + f"Running Demucs Inference...{space}\n") - else: - widget_text.write(base_text + f"Running Demucs Inference... ") - print(' Running Demucs Inference...') - - mix = torch.tensor(mix, dtype=torch.float32) - ref = mix.mean(0) - mix = (mix - ref.mean()) / ref.std() - - with torch.no_grad(): - sources = apply_model(self.demucs, - mix[None], - gui_progress_bar, - widget_text, - update_prog, - split=split_mode, - device=device, - overlap=overlap_set, - shifts=shift_set, - progress=False, - segmen=True, - **progress_demucs_kwargs)[0] - - if split_mode: - widget_text.write('\n') - else: - widget_text.write('Done!\n') - - sources = (sources * ref.std() + ref.mean()).cpu().numpy() - sources[[0,1]] = sources[[1,0]] - - return sources - - def demix_demucs_v1(self, mix, margin_size): - processed = {} - demucsitera = len(mix) - demucsitera_calc = demucsitera * 2 - gui_progress_bar_demucs = 0 - progress_bar = 0 - print(' Running Demucs Inference...') - if demucsitera == 1: - widget_text.write(base_text + f"Running Demucs v1 Inference... ") - else: - widget_text.write(base_text + f"Running Demucs v1 Inference...{space}\n") - for nmix in mix: - gui_progress_bar_demucs += 1 - progress_bar += 100 - step = (progress_bar / demucsitera) - if demucsitera == 1: - pass - else: - percent_prog = f"{base_text}Demucs v1 Inference Progress: {gui_progress_bar_demucs}/{demucsitera} | {round(step)}%" - widget_text.percentage(percent_prog) - update_progress(**progress_kwargs, - step=(0.35 + (1.05/demucsitera_calc * gui_progress_bar_demucs))) - cmix = mix[nmix] - cmix = torch.tensor(cmix, dtype=torch.float32) - ref = cmix.mean(0) - cmix = (cmix - ref.mean()) / ref.std() - with torch.no_grad(): - sources = apply_model_v1(self.demucs, - cmix.to(device), - gui_progress_bar, - widget_text, - update_prog, - split=split_mode, - segmen=False, - shifts=shift_set, - **progress_demucs_kwargs) - sources = (sources * ref.std() + ref.mean()).cpu().numpy() - sources[[0,1]] = sources[[1,0]] - - start = 0 if nmix == 0 else margin_size - end = None if nmix == list(mix.keys())[::-1][0] else -margin_size - if margin_size == 0: - end = None - processed[nmix] = sources[:,:,start:end].copy() - - sources = list(processed.values()) - sources = np.concatenate(sources, axis=-1) - - if demucsitera == 1: - widget_text.write('Done!\n') - else: - widget_text.write('\n') - - return sources - - def demix_demucs_v1_split(self, mix): - - print(' Running Demucs Inference...') - if split_mode: - widget_text.write(base_text + f"Running Demucs v1 Inference...{space}\n") - else: - widget_text.write(base_text + f"Running Demucs v1 Inference... ") - - mix = torch.tensor(mix, dtype=torch.float32) - ref = mix.mean(0) - mix = (mix - ref.mean()) / ref.std() - - with torch.no_grad(): - sources = apply_model_v1(self.demucs, - mix.to(device), - gui_progress_bar, - widget_text, - update_prog, - split=split_mode, - segmen=True, - shifts=shift_set, - **progress_demucs_kwargs) - sources = (sources * ref.std() + ref.mean()).cpu().numpy() - sources[[0,1]] = sources[[1,0]] - - if split_mode: - widget_text.write('\n') - else: - widget_text.write('Done!\n') - - return sources - - def demix_demucs_v2(self, mix, margin_size): - processed = {} - demucsitera = len(mix) - demucsitera_calc = demucsitera * 2 - gui_progress_bar_demucs = 0 - progress_bar = 0 - if demucsitera == 1: - widget_text.write(base_text + f"Running Demucs v2 Inference... ") - else: - widget_text.write(base_text + f"Running Demucs v2 Inference...{space}\n") - - for nmix in mix: - gui_progress_bar_demucs += 1 - progress_bar += 100 - step = (progress_bar / demucsitera) - if demucsitera == 1: - pass - else: - percent_prog = f"{base_text}Demucs v2 Inference Progress: {gui_progress_bar_demucs}/{demucsitera} | {round(step)}%" - widget_text.percentage(percent_prog) - - update_progress(**progress_kwargs, - step=(0.35 + (1.05/demucsitera_calc * gui_progress_bar_demucs))) - cmix = mix[nmix] - cmix = torch.tensor(cmix, dtype=torch.float32) - ref = cmix.mean(0) - cmix = (cmix - ref.mean()) / ref.std() - with torch.no_grad(): - sources = apply_model_v2(self.demucs, - cmix.to(device), - gui_progress_bar, - widget_text, - update_prog, - split=split_mode, - segmen=False, - overlap=overlap_set, - shifts=shift_set, - **progress_demucs_kwargs) - sources = (sources * ref.std() + ref.mean()).cpu().numpy() - sources[[0,1]] = sources[[1,0]] - - start = 0 if nmix == 0 else margin_size - end = None if nmix == list(mix.keys())[::-1][0] else -margin_size - if margin_size == 0: - end = None - processed[nmix] = sources[:,:,start:end].copy() - - sources = list(processed.values()) - sources = np.concatenate(sources, axis=-1) - - if demucsitera == 1: - widget_text.write('Done!\n') - else: - widget_text.write('\n') - - return sources - - def demix_demucs_v2_split(self, mix): - print(' Running Demucs Inference...') - - if split_mode: - widget_text.write(base_text + f"Running Demucs v2 Inference...{space}\n") - else: - widget_text.write(base_text + f"Running Demucs v2 Inference... ") - - mix = torch.tensor(mix, dtype=torch.float32) - ref = mix.mean(0) - mix = (mix - ref.mean()) / ref.std() - with torch.no_grad(): - sources = apply_model_v2(self.demucs, - mix.to(device), - gui_progress_bar, - widget_text, - update_prog, - split=split_mode, - segmen=True, - overlap=overlap_set, - shifts=shift_set, - **progress_demucs_kwargs) - - sources = (sources * ref.std() + ref.mean()).cpu().numpy() - sources[[0,1]] = sources[[1,0]] - - if split_mode: - widget_text.write('\n') - else: - widget_text.write('Done!\n') - - return sources - - -data = { - 'autocompensate': True, - 'aud_mdx': True, - 'bit': '', - 'chunks': 10, - 'compensate': 1.03597672895, - 'demucs_only': False, - 'demucsmodel': False, - 'DemucsModel_MDX': 'UVR_Demucs_Model_1', - 'dim_f': 2048, - 'export_path': None, - 'flactype': 'PCM_16', - 'gpu': -1, - 'input_paths': None, - 'inst_only': False, - 'margin': 44100, - 'mdxnetModel': 'UVR-MDX-NET Main', - 'mdxnetModeltype': 'Vocals (Custom)', - 'mixing': 'Default', - 'modelFolder': False, - 'mp3bit': '320k', - 'n_fft_scale': 6144, - 'no_chunk': False, - 'noise_pro_select': 'Auto Select', - 'noisereduc_s': 3, - 'non_red': False, - 'nophaseinst': True, - 'normalize': False, - 'overlap': 0.5, - 'saveFormat': 'Wav', - 'segment': 'Default', - 'shifts': 0, - 'split_mode': False, - 'voc_only': False, - 'wavtype': 'PCM_16', -} - -default_chunks = data['chunks'] -default_noisereduc_s = data['noisereduc_s'] - -def update_progress(progress_var, total_files, file_num, step: float = 1): - """Calculate the progress for the progress widget in the GUI""" - base = (100 / total_files) - progress = base * (file_num - 1) - progress += base * step - - progress_var.set(progress) - -def get_baseText(total_files, file_num): - """Create the base text for the command widget""" - text = 'File {file_num}/{total_files} '.format(file_num=file_num, - total_files=total_files) - return text - -warnings.filterwarnings("ignore") -cpu = torch.device('cpu') - -def hide_opt(): - with open(os.devnull, "w") as devnull: - old_stdout = sys.stdout - sys.stdout = devnull - try: - yield - finally: - sys.stdout = old_stdout - -def main(window: tk.Wm, - text_widget: tk.Text, - button_widget: tk.Button, - progress_var: tk.Variable, - stop_thread, - **kwargs: dict): - - global widget_text - global gui_progress_bar - global music_file - global default_chunks - global default_noisereduc_s - global _basename - global _mixture - global modeltype - global n_fft_scale_set - global dim_f_set - global progress_kwargs - global progress_demucs_kwargs - global base_text - global model_set_name - global mdx_model_name - global stemset_n - global stem_text_a - global stem_text_b - global noise_pro_set - global demucs_model_set - global autocompensate - global compensate - global channel_set - global margin_set - global overlap_set - global shift_set - global source_val - global split_mode - global demucs_model_set - global wav_type_set - global flac_type_set - global mp3_bit_set - global normalization_set - global demucs_model_version - global mdx_model_path - global widget_button - global stime - global model_hash - global demucs_switch - global no_chunk_demucs - global inst_only - global voc_only - global space - global main_window - global stop_button - - stop_button = stop_thread - - widget_text = text_widget - gui_progress_bar = progress_var - widget_button = button_widget - main_window = window - - - #Error Handling - - onnxmissing = "[ONNXRuntimeError] : 3 : NO_SUCHFILE" - onnxmemerror = "onnxruntime::CudaCall CUDA failure 2: out of memory" - onnxmemerror2 = "onnxruntime::BFCArena::AllocateRawInternal" - systemmemerr = "DefaultCPUAllocator: not enough memory" - runtimeerr = "CUDNN error executing cudnnSetTensorNdDescriptor" - cuda_err = "CUDA out of memory" - mod_err = "ModuleNotFoundError" - file_err = "FileNotFoundError" - ffmp_err = """audioread\__init__.py", line 116, in audio_open""" - sf_write_err = "sf.write" - model_adv_set_err = "Got invalid dimensions for input" - demucs_model_missing_err = "is neither a single pre-trained model or a bag of models." - - try: - with open('errorlog.txt', 'w') as f: - f.write(f'No errors to report at this time.' + f'\n\nLast Process Method Used: MDX-Net' + - f'\nLast Conversion Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - pass - - data.update(kwargs) - - global update_prog - - # Update default settings - update_prog = update_progress - default_chunks = data['chunks'] - default_noisereduc_s = data['noisereduc_s'] - no_chunk_demucs = data['no_chunk'] - space = ' '*90 - - if data['DemucsModel_MDX'] == "Tasnet v1": - demucs_model_set_name = 'tasnet.th' - demucs_model_version = 'v1' - elif data['DemucsModel_MDX'] == "Tasnet_extra v1": - demucs_model_set_name = 'tasnet_extra.th' - demucs_model_version = 'v1' - elif data['DemucsModel_MDX'] == "Demucs v1": - demucs_model_set_name = 'demucs.th' - demucs_model_version = 'v1' - elif data['DemucsModel_MDX'] == "Demucs v1.gz": - demucs_model_set_name = 'demucs.th.gz' - demucs_model_version = 'v1' - elif data['DemucsModel_MDX'] == "Demucs_extra v1": - demucs_model_set_name = 'demucs_extra.th' - demucs_model_version = 'v1' - elif data['DemucsModel_MDX'] == "Demucs_extra v1.gz": - demucs_model_set_name = 'demucs_extra.th.gz' - demucs_model_version = 'v1' - elif data['DemucsModel_MDX'] == "Light v1": - demucs_model_set_name = 'light.th' - demucs_model_version = 'v1' - elif data['DemucsModel_MDX'] == "Light v1.gz": - demucs_model_set_name = 'light.th.gz' - demucs_model_version = 'v1' - elif data['DemucsModel_MDX'] == "Light_extra v1": - demucs_model_set_name = 'light_extra.th' - demucs_model_version = 'v1' - elif data['DemucsModel_MDX'] == "Light_extra v1.gz": - demucs_model_set_name = 'light_extra.th.gz' - demucs_model_version = 'v1' - elif data['DemucsModel_MDX'] == "Tasnet v2": - demucs_model_set_name = 'tasnet-beb46fac.th' - demucs_model_version = 'v2' - elif data['DemucsModel_MDX'] == "Tasnet_extra v2": - demucs_model_set_name = 'tasnet_extra-df3777b2.th' - demucs_model_version = 'v2' - elif data['DemucsModel_MDX'] == "Demucs48_hq v2": - demucs_model_set_name = 'demucs48_hq-28a1282c.th' - demucs_model_version = 'v2' - elif data['DemucsModel_MDX'] == "Demucs v2": - demucs_model_set_name = 'demucs-e07c671f.th' - demucs_model_version = 'v2' - elif data['DemucsModel_MDX'] == "Demucs_extra v2": - demucs_model_set_name = 'demucs_extra-3646af93.th' - demucs_model_version = 'v2' - elif data['DemucsModel_MDX'] == "Demucs_unittest v2": - demucs_model_set_name = 'demucs_unittest-09ebc15f.th' - demucs_model_version = 'v2' - elif '.ckpt' in data['DemucsModel_MDX'] and 'v2' in data['DemucsModel_MDX']: - demucs_model_set_name = data['DemucsModel_MDX'] - demucs_model_version = 'v2' - elif '.ckpt' in data['DemucsModel_MDX'] and 'v1' in data['DemucsModel_MDX']: - demucs_model_set_name = data['DemucsModel_MDX'] - demucs_model_version = 'v1' - elif '.gz' in data['DemucsModel_MDX']: - demucs_model_set_name = data['DemucsModel_MDX'] - demucs_model_version = 'v1' - else: - demucs_model_set_name = data['DemucsModel_MDX'] - demucs_model_version = 'v3' - - autocompensate = data['autocompensate'] - - model_set_name = data['mdxnetModel'] - - if model_set_name == 'UVR-MDX-NET 1': - mdx_model_name = 'UVR_MDXNET_1_9703' - elif model_set_name == 'UVR-MDX-NET 2': - mdx_model_name = 'UVR_MDXNET_2_9682' - elif model_set_name == 'UVR-MDX-NET 3': - mdx_model_name = 'UVR_MDXNET_3_9662' - elif model_set_name == 'UVR-MDX-NET Karaoke': - mdx_model_name = 'UVR_MDXNET_KARA' - elif model_set_name == 'UVR-MDX-NET Main': - mdx_model_name = 'UVR_MDXNET_Main' - elif model_set_name == 'UVR-MDX-NET Inst 1': - mdx_model_name = 'UVR_MDXNET_Inst_1' - elif model_set_name == 'UVR-MDX-NET Inst 2': - mdx_model_name = 'UVR_MDXNET_Inst_2' - else: - mdx_model_name = data['mdxnetModel'] - - - mdx_model_path = f'models/MDX_Net_Models/{mdx_model_name}.onnx' - - model_hash = hashlib.md5(open(mdx_model_path,'rb').read()).hexdigest() - model_params = [] - model_params = lib_v5.filelist.provide_mdx_model_param_name(model_hash) - - modeltype = model_params[0] - noise_pro = model_params[1] - stemset_n = model_params[2] - compensate_set = model_params[3] - source_val = model_params[4] - n_fft_scale_set = model_params[5] - dim_f_set = model_params[6] - - if not data['aud_mdx']: - if data['mdxnetModeltype'] == 'Vocals (Custom)': - modeltype = 'v' - source_val = 3 - stemset_n = '(Vocals)' - n_fft_scale_set = data['n_fft_scale'] - dim_f_set = data['dim_f'] - if data['mdxnetModeltype'] == 'Instrumental (Custom)': - modeltype = 'v' - source_val = 0 - stemset_n = '(Instrumental)' - n_fft_scale_set = data['n_fft_scale'] - dim_f_set = data['dim_f'] - if data['mdxnetModeltype'] == 'Other (Custom)': - modeltype = 'v' - source_val = 2 - stemset_n = '(Other)' - n_fft_scale_set = data['n_fft_scale'] - dim_f_set = data['dim_f'] - if data['mdxnetModeltype'] == 'Drums (Custom)': - modeltype = 'v' - source_val = 1 - stemset_n = '(Drums)' - n_fft_scale_set = data['n_fft_scale'] - dim_f_set = data['dim_f'] - if data['mdxnetModeltype'] == 'Bass (Custom)': - modeltype = 'v' - source_val = 0 - stemset_n = '(Bass)' - n_fft_scale_set = data['n_fft_scale'] - dim_f_set = data['dim_f'] - - if stemset_n == '(Vocals)': - stem_text_a = 'Vocals' - stem_text_b = 'Instrumental' - elif stemset_n == '(Instrumental)': - stem_text_a = 'Instrumental' - stem_text_b = 'Vocals' - elif stemset_n == '(Other)': - stem_text_a = 'Other' - stem_text_b = 'the no \"Other\" track' - elif stemset_n == '(Drums)': - stem_text_a = 'Drums' - stem_text_b = 'the no \"Drums\" track' - elif stemset_n == '(Bass)': - stem_text_a = 'Bass' - stem_text_b = 'the no \"Bass\" track' - else: - stem_text_a = 'Vocals' - stem_text_b = 'Instrumental' - - if autocompensate: - compensate = compensate_set - else: - compensate = data['compensate'] - - if data['noise_pro_select'] == 'Auto Select': - noise_pro_set = noise_pro - else: - noise_pro_set = data['noise_pro_select'] - - if data['wavtype'] == '32-bit Float': - wav_type_set = 'FLOAT' - elif data['wavtype'] == '64-bit Float': - wav_type_set = 'DOUBLE' - else: - wav_type_set = data['wavtype'] - - flac_type_set = data['flactype'] - mp3_bit_set = data['mp3bit'] - - if data['normalize'] == True: - normalization_set = spec_utils.normalize - #print('normalization on') - else: - normalization_set = spec_utils.nonormalize - #print('normalization off') - - #print(n_fft_scale_set) - #print(dim_f_set) - #print(demucs_model_set_name) - - inst_only = data['inst_only'] - voc_only = data['voc_only'] - - stime = time.perf_counter() - progress_var.set(0) - text_widget.clear() - button_widget.configure(state=tk.DISABLED) # Disable Button - - try: #Load File(s) - for file_num, music_file in tqdm(enumerate(data['input_paths'], start=1)): - - overlap_set = float(data['overlap']) - channel_set = int(data['channel']) - margin_set = int(data['margin']) - shift_set = int(data['shifts']) - demucs_model_set = demucs_model_set_name - split_mode = data['split_mode'] - demucs_switch = data['demucsmodel'] - - if data['wavtype'] == '64-bit Float': - if data['saveFormat'] == 'Flac': - text_widget.write('Please select \"WAV\" as your save format to use 64-bit Float.\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - if data['wavtype'] == '64-bit Float': - if data['saveFormat'] == 'Mp3': - text_widget.write('Please select \"WAV\" as your save format to use 64-bit Float.\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - _mixture = f'{data["input_paths"]}' - - timestampnum = round(datetime.utcnow().timestamp()) - randomnum = randrange(100000, 1000000) - - if data['settest']: - try: - _basename = f'{data["export_path"]}/{str(timestampnum)}_{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}' - except: - _basename = f'{data["export_path"]}/{str(randomnum)}_{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}' - else: - _basename = f'{data["export_path"]}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}' - - - inference_type = 'inference_mdx' - - # -Get text and update progress- - base_text = get_baseText(total_files=len(data['input_paths']), - file_num=file_num) - progress_kwargs = {'progress_var': progress_var, - 'total_files': len(data['input_paths']), - 'file_num': file_num} - progress_demucs_kwargs = {'total_files': len(data['input_paths']), - 'file_num': file_num, 'inference_type': inference_type} - - - if 'UVR' in demucs_model_set: - if stemset_n == '(Bass)' or stemset_n == '(Drums)' or stemset_n == '(Other)': - widget_text.write('The selected Demucs model can only be used with vocal or instrumental stems.\n') - widget_text.write('Please select a 4 stem Demucs model and try again.\n\n') - widget_text.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - gui_progress_bar.set(0) - widget_button.configure(state=tk.NORMAL) # Enable Button - return - - - if stemset_n == '(Instrumental)': - if not 'UVR' in demucs_model_set: - if data['demucsmodel']: - widget_text.write(base_text + 'The selected Demucs model cannot be used with this model.\n') - widget_text.write(base_text + 'Only 2 stem Demucs models are compatible with this model.\n') - widget_text.write(base_text + 'Setting Demucs model to \"UVR_Demucs_Model_1\".\n\n') - demucs_model_set = 'UVR_Demucs_Model_1' - - try: - if float(data['noisereduc_s']) >= 11: - text_widget.write('Error: Noise Reduction only supports values between 0-10.\nPlease set a value between 0-10 (with or without decimals) and try again.') - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - total, used, free = shutil.disk_usage("/") - - - total_space = int(total/1.074e+9) - used_space = int(used/1.074e+9) - free_space = int(free/1.074e+9) - - - if int(free/1.074e+9) <= int(2): - text_widget.write('Error: Not enough storage on main drive to continue. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly. \n\nPlease ensure your main drive has at least 3 GB\'s of storage and try again.\n\n') - text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n') - text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n') - text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n') - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - if int(free/1.074e+9) in [3, 4, 5, 6, 7, 8]: - text_widget.write('Warning: Your main drive is running low on storage. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly.\n\n') - text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n') - text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n') - text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n\n') - except: - pass - - if data['noisereduc_s'] == 'None': - pass - else: - if not os.path.isfile("lib_v5\sox\sox.exe"): - data['noisereduc_s'] = 'None' - data['non_red'] = False - widget_text.write(base_text + 'SoX is missing and required for noise reduction.\n') - widget_text.write(base_text + 'See the \"More Info\" tab in the Help Guide.\n') - widget_text.write(base_text + 'Noise Reduction will be disabled until SoX is available.\n\n') - - update_progress(**progress_kwargs, - step=0) - - e = os.path.join(data["export_path"]) - - demucsmodel = 'models/Demucs_Models/' + str(data['DemucsModel_MDX']) - - pred = Predictor() - - - print('\n\nmodeltype: ', modeltype) - print('noise_pro: ', noise_pro) - print('stemset_n: ', stemset_n) - print('compensate_set: ', compensate_set) - print('source_val: ', source_val) - print('n_fft_scale_set: ', n_fft_scale_set) - print('dim_f_set: ', dim_f_set, '\n') - - if modeltype == 'Not Set' or \ - noise_pro == 'Not Set' or \ - stemset_n == 'Not Set' or \ - compensate_set == 'Not Set' or \ - source_val == 'Not Set' or \ - n_fft_scale_set == 'Not Set' or \ - dim_f_set == 'Not Set': - confirm = tk.messagebox.askyesno(title='Unrecognized Model Detected', - message=f'\nWould you like to set the correct model parameters for this model before continuing?\n') - - if confirm: - pred.mdx_options() - else: - text_widget.write(f'An unrecognized model has been detected.\n\n') - text_widget.write(f'Please configure the ONNX model settings accordingly and try again.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - torch.cuda.empty_cache() - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - pred.prediction_setup() - - #print(demucsmodel) - - # split - pred.prediction( - m=music_file, - ) - - except Exception as e: - traceback_text = ''.join(traceback.format_tb(e.__traceback__)) - message = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' - if runtimeerr in message: - text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') - text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') - text_widget.write(f'\nError Received:\n\n') - text_widget.write(f'Your PC cannot process this audio file with the chunk size selected.\nPlease lower the chunk size and try again.\n\n') - text_widget.write(f'If this error persists, please contact the developers.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while processing "{os.path.basename(music_file)}":\n' + - f'Process Method: MDX-Net\n\n' + - f'Your PC cannot process this audio file with the chunk size selected.\nPlease lower the chunk size and try again.\n\n' + - f'If this error persists, please contact the developers.\n\n' + - f'Raw error details:\n\n' + - message + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - pass - torch.cuda.empty_cache() - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - if cuda_err in message: - text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') - text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') - text_widget.write(f'\nError Received:\n\n') - text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n') - text_widget.write(f'Please close any GPU intensive applications and try again.\n') - text_widget.write(f'If the error persists, your GPU might not be supported.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while processing "{os.path.basename(music_file)}":\n' + - f'Process Method: MDX-Net\n\n' + - f'The application was unable to allocate enough GPU memory to use this model.\n' + - f'Please close any GPU intensive applications and try again.\n' + - f'If the error persists, your GPU might not be supported.\n\n' + - f'Raw error details:\n\n' + - message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - pass - torch.cuda.empty_cache() - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - if mod_err in message: - text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') - text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') - text_widget.write(f'\nError Received:\n\n') - text_widget.write(f'Application files(s) are missing.\n') - text_widget.write("\n" + f'{type(e).__name__} - "{e}"' + "\n\n") - text_widget.write(f'Please check for missing files/scripts in the app directory and try again.\n') - text_widget.write(f'If the error persists, please reinstall application or contact the developers.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while processing "{os.path.basename(music_file)}":\n' + - f'Process Method: MDX-Net\n\n' + - f'Application files(s) are missing.\n' + - f'Please check for missing files/scripts in the app directory and try again.\n' + - f'If the error persists, please reinstall application or contact the developers.\n\n' + - message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - pass - torch.cuda.empty_cache() - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - if file_err in message: - text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') - text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') - text_widget.write(f'\nError Received:\n\n') - text_widget.write(f'Missing file error raised.\n') - text_widget.write("\n" + f'{type(e).__name__} - "{e}"' + "\n\n") - text_widget.write("\n" + f'Please address the error and try again.' + "\n") - text_widget.write(f'If this error persists, please contact the developers.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - torch.cuda.empty_cache() - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while processing "{os.path.basename(music_file)}":\n' + - f'Process Method: MDX-Net\n\n' + - f'Missing file error raised.\n' + - "\n" + f'Please address the error and try again.' + "\n" + - f'If this error persists, please contact the developers.\n\n' + - message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - pass - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - if ffmp_err in message: - text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') - text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') - text_widget.write(f'\nError Received:\n\n') - text_widget.write(f'The input file type is not supported or FFmpeg is missing.\n') - text_widget.write(f'Please select a file type supported by FFmpeg and try again.\n\n') - text_widget.write(f'If FFmpeg is missing or not installed, you will only be able to process \".wav\" files \nuntil it is available on this system.\n\n') - text_widget.write(f'See the \"More Info\" tab in the Help Guide.\n\n') - text_widget.write(f'If this error persists, please contact the developers.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - torch.cuda.empty_cache() - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while processing "{os.path.basename(music_file)}":\n' + - f'Process Method: MDX-Net\n\n' + - f'The input file type is not supported or FFmpeg is missing.\nPlease select a file type supported by FFmpeg and try again.\n\n' + - f'If FFmpeg is missing or not installed, you will only be able to process \".wav\" files until it is available on this system.\n\n' + - f'See the \"More Info\" tab in the Help Guide.\n\n' + - f'If this error persists, please contact the developers.\n\n' + - message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - pass - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - if onnxmissing in message: - text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') - text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') - text_widget.write(f'\nError Received:\n\n') - text_widget.write(f'The application could not detect this MDX-Net model on your system.\n') - text_widget.write(f'Please make sure all the models are present in the correct directory.\n') - text_widget.write(f'If the error persists, please reinstall application or contact the developers.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while processing "{os.path.basename(music_file)}":\n' + - f'Process Method: MDX-Net\n\n' + - f'The application could not detect this MDX-Net model on your system.\n' + - f'Please make sure all the models are present in the correct directory.\n' + - f'If the error persists, please reinstall application or contact the developers.\n\n' + - message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - pass - torch.cuda.empty_cache() - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - if onnxmemerror in message: - text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') - text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') - text_widget.write(f'\nError Received:\n\n') - text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n') - text_widget.write(f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n') - text_widget.write(f'If the error persists, your GPU might not be supported.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while processing "{os.path.basename(music_file)}":\n' + - f'Process Method: MDX-Net\n\n' + - f'The application was unable to allocate enough GPU memory to use this model.\n' + - f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n' + - f'If the error persists, your GPU might not be supported.\n\n' + - message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - pass - torch.cuda.empty_cache() - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - if onnxmemerror2 in message: - text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') - text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') - text_widget.write(f'\nError Received:\n\n') - text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n') - text_widget.write(f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n') - text_widget.write(f'If the error persists, your GPU might not be supported.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while processing "{os.path.basename(music_file)}":\n' + - f'Process Method: MDX-Net\n\n' + - f'The application was unable to allocate enough GPU memory to use this model.\n' + - f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n' + - f'If the error persists, your GPU might not be supported.\n\n' + - message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - pass - torch.cuda.empty_cache() - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - if sf_write_err in message: - text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') - text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') - text_widget.write(f'\nError Received:\n\n') - text_widget.write(f'Could not write audio file.\n') - text_widget.write(f'This could be due to low storage on target device or a system permissions issue.\n') - text_widget.write(f"\nGo to the Settings Menu and click \"Open Error Log\" for raw error details.\n") - text_widget.write(f'\nIf the error persists, please contact the developers.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while processing "{os.path.basename(music_file)}":\n' + - f'Process Method: MDX-Net\n\n' + - f'Could not write audio file.\n' + - f'This could be due to low storage on target device or a system permissions issue.\n' + - f'If the error persists, please contact the developers.\n\n' + - message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - pass - torch.cuda.empty_cache() - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - if systemmemerr in message: - text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') - text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') - text_widget.write(f'\nError Received:\n\n') - text_widget.write(f'The application was unable to allocate enough system memory to use this \nmodel.\n\n') - text_widget.write(f'Please do the following:\n\n1. Restart this application.\n2. Ensure any CPU intensive applications are closed.\n3. Then try again.\n\n') - text_widget.write(f'Please Note: Intel Pentium and Intel Celeron processors do not work well with \nthis application.\n\n') - text_widget.write(f'If the error persists, the system may not have enough RAM, or your CPU might \nnot be supported.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while processing "{os.path.basename(music_file)}":\n' + - f'Process Method: MDX-Net\n\n' + - f'The application was unable to allocate enough system memory to use this model.\n' + - f'Please do the following:\n\n1. Restart this application.\n2. Ensure any CPU intensive applications are closed.\n3. Then try again.\n\n' + - f'Please Note: Intel Pentium and Intel Celeron processors do not work well with this application.\n\n' + - f'If the error persists, the system may not have enough RAM, or your CPU might \nnot be supported.\n\n' + - message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - pass - torch.cuda.empty_cache() - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - if model_adv_set_err in message: - text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') - text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') - text_widget.write(f'\nError Received:\n\n') - text_widget.write(f'The current ONNX model settings are not compatible with the selected \nmodel.\n\n') - text_widget.write(f'Please re-configure the advanced ONNX model settings accordingly and try \nagain.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while processing "{os.path.basename(music_file)}":\n' + - f'Process Method: MDX-Net\n\n' + - f'The current ONNX model settings are not compatible with the selected model.\n\n' + - f'Please re-configure the advanced ONNX model settings accordingly and try again.\n\n' + - message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - pass - torch.cuda.empty_cache() - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - if demucs_model_missing_err in message: - text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') - text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') - text_widget.write(f'\nError Received:\n\n') - text_widget.write(f'The selected Demucs model is missing.\n\n') - text_widget.write(f'Please download the model or make sure it is in the correct directory.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while processing "{os.path.basename(music_file)}":\n' + - f'Process Method: MDX-Net\n\n' + - f'The selected Demucs model is missing.\n\n' + - f'Please download the model or make sure it is in the correct directory.\n\n' + - message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - pass - torch.cuda.empty_cache() - progress_var.set(0) - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - - print(traceback_text) - print(type(e).__name__, e) - print(message) - - try: - with open('errorlog.txt', 'w') as f: - f.write(f'Last Error Received:\n\n' + - f'Error Received while processing "{os.path.basename(music_file)}":\n' + - f'Process Method: MDX-Net\n\n' + - f'If this error persists, please contact the developers with the error details.\n\n' + - message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') - except: - tk.messagebox.showerror(master=window, - title='Error Details', - message=message) - progress_var.set(0) - text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') - text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') - text_widget.write(f'\nError Received:\n') - text_widget.write("\nGo to the Settings Menu and click \"Open Error Log\" for raw error details.\n") - text_widget.write("\n" + f'Please address the error and try again.' + "\n") - text_widget.write(f'If this error persists, please contact the developers with the error details.\n\n') - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') - try: - torch.cuda.empty_cache() - except: - pass - button_widget.configure(state=tk.NORMAL) # Enable Button - return - - progress_var.set(0) - - text_widget.write(f'\nConversion(s) Completed!\n') - - text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') # nopep8 - torch.cuda.empty_cache() - button_widget.configure(state=tk.NORMAL) # Enable Button - -if __name__ == '__main__': - start_time = time.time() - main() - print("Successfully completed music demixing.");print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1)) -