ultimatevocalremovergui/inference_MDX.py

import os
from pickle import STOP
from tracemalloc import stop
from turtle import update
import subprocess
from unittest import skip
from pathlib import Path
import os.path
from datetime import datetime
import pydub
import shutil
#MDX-Net
#----------------------------------------
import soundfile as sf
import torch
import numpy as np
from demucs.model import Demucs
from demucs.utils import apply_model
from models import get_models, spec_effects
import onnxruntime as ort
import time
import os
from tqdm import tqdm
import warnings
import sys
import librosa
import psutil
#----------------------------------------
from lib_v5 import spec_utils
from lib_v5.model_param_init import ModelParameters
import torch

# Command line text parsing and widget manipulation
import tkinter as tk
import traceback  # Error Message Recent Calls
import time  # Timer
    
class Predictor():        
    def __init__(self):
        pass
    
    def prediction_setup(self, demucs_name,
                               channels=64):
        if data['demucsmodel']:
            self.demucs = Demucs(sources=["drums", "bass", "other", "vocals"], channels=channels)
            widget_text.write(base_text + 'Loading Demucs model... ')
            update_progress(**progress_kwargs,
            step=0.05)   
            self.demucs.to(device)
            self.demucs.load_state_dict(torch.load(demucs_name))
            widget_text.write('Done!\n')
            self.demucs.eval()
        self.onnx_models = {}
        c = 0
        
        self.models = get_models('tdf_extra', load=False, device=cpu, stems='vocals')
        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']     
        else:
            run_type = ['CPUExecutionProvider']

        self.onnx_models[c] = ort.InferenceSession(os.path.join('models/MDX_Net_Models', model_set), providers=run_type)
        widget_text.write('Done!\n')
        
    def prediction(self, m):  
        #mix, rate = sf.read(m)
        mix, rate = librosa.load(m, mono=False, sr=44100)
        if mix.ndim == 1:
            mix = np.asfortranarray([mix,mix])
        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)
        
        #Vocal Path
        vocal_name = '(Vocals)'
        if data['modelFolder']:
            vocal_path = '{save_path}/{file_name}.wav'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{vocal_name}_{model_set_name}',)
            vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{vocal_name}_{model_set_name}',)
            vocal_path_flac = '{save_path}/{file_name}.flac'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{vocal_name}_{model_set_name}',)
        else:
            vocal_path = '{save_path}/{file_name}.wav'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{vocal_name}',)
            vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{vocal_name}',)
            vocal_path_flac = '{save_path}/{file_name}.flac'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{vocal_name}',)
        
        #Instrumental Path
        Instrumental_name = '(Instrumental)'
        if data['modelFolder']:
            Instrumental_path = '{save_path}/{file_name}.wav'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{model_set_name}',)
            Instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{model_set_name}',)
            Instrumental_path_flac = '{save_path}/{file_name}.flac'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{model_set_name}',)            
        else: 
            Instrumental_path = '{save_path}/{file_name}.wav'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{Instrumental_name}',)
            Instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{Instrumental_name}',)
            Instrumental_path_flac = '{save_path}/{file_name}.flac'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{Instrumental_name}',)   
                     
        #Non-Reduced Vocal Path
        vocal_name = '(Vocals)'
        if data['modelFolder']:
            non_reduced_vocal_path = '{save_path}/{file_name}.wav'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{vocal_name}_{model_set_name}_No_Reduction',)
            non_reduced_vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{vocal_name}_{model_set_name}_No_Reduction',)
            non_reduced_vocal_path_flac = '{save_path}/{file_name}.flac'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{vocal_name}_{model_set_name}_No_Reduction',)
        else:
            non_reduced_vocal_path = '{save_path}/{file_name}.wav'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{vocal_name}_No_Reduction',)
            non_reduced_vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{vocal_name}_No_Reduction',)
            non_reduced_vocal_path_flac = '{save_path}/{file_name}.flac'.format(
                save_path=save_path,
                file_name = f'{os.path.basename(_basename)}_{vocal_name}_No_Reduction',)
            
            
        if 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 data['inst_only']:
                    widget_text.write(base_text + 'Preparing to save Instrumental...')
                else:
                    widget_text.write(base_text + 'Saving vocals... ')

                sf.write(non_reduced_vocal_path, sources[c].T, rate)
                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\\mdxnetnoisereduc.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 data['inst_only']:
                    widget_text.write(base_text + 'Preparing Instrumental...')
                else:
                    widget_text.write(base_text + 'Saving Vocals... ')

                sf.write(non_reduced_vocal_path, sources[3].T, rate)
                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\\mdxnetnoisereduc.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 data['inst_only']:
                    widget_text.write(base_text + 'Preparing Instrumental...')
                else:
                    widget_text.write(base_text + 'Saving Vocals... ')
                sf.write(vocal_path, sources[c].T, rate)
                update_progress(**progress_kwargs,
                step=(0.9))
                widget_text.write('Done!\n')
            else:
                if data['inst_only']:
                    widget_text.write(base_text + 'Preparing Instrumental...')
                else:
                    widget_text.write(base_text + 'Saving Vocals... ')
                sf.write(vocal_path, sources[3].T, rate)
                update_progress(**progress_kwargs,
                step=(0.9))
                widget_text.write('Done!\n')
        
        if data['voc_only'] and not data['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 + 'Saving Instrumental... ')      
            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))
                sf.write(Instrumental_path, spec_utils.cmb_spectrogram_to_wave(-v_spec, mp), mp.param['sr'])
                
                    
                if data['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 data['inst_only'] == True:
                    pass
                else:
                    musfile = pydub.AudioSegment.from_wav(vocal_path)
                    musfile.export(vocal_path_mp3, format="mp3", bitrate="320k")
                    if file_exists_v == 'there':
                        pass
                    else:
                        try:
                            os.remove(vocal_path)
                        except:
                            pass
                if data['voc_only'] == True:
                    pass
                else:
                    musfile = pydub.AudioSegment.from_wav(Instrumental_path)
                    musfile.export(Instrumental_path_mp3, format="mp3", bitrate="320k")
                    if file_exists_i == 'there':
                        pass
                    else:
                        try:
                            os.remove(Instrumental_path)
                        except:
                            pass
                    if data['non_red'] == True:
                        musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path)
                        musfile.export(non_reduced_vocal_path_mp3, format="mp3", bitrate="320k") 
                        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 data['inst_only'] == True:
                    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['voc_only'] == True:
                    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:
                        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
        
        
        try:
            print('Is there already a voc file there? ', file_exists_v)
            print('Is there already a non_voc file there? ', file_exists_n)
        except: 
            pass

        

        if data['noisereduc_s'] == 'None':
            pass
        elif data['non_red'] == True:
            pass
        elif data['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 Seperation!\n')

    def demix(self, mix):
        # 1 = demucs only
        # 0 = onnx only
        if data['chunks'] == 'Full':
            chunk_set = 0
        else: 
            chunk_set = data['chunks']

        if data['chunks'] == 'Auto':
            if data['gpu'] == 0:
                try:
                    gpu_mem = round(torch.cuda.get_device_properties(0).total_memory/1.074e+9)
                except:
                    widget_text.write(base_text + 'NVIDIA GPU Required for conversion!\n')
                if int(gpu_mem) <= int(5):
                    chunk_set = int(5)
                    widget_text.write(base_text + 'Chunk size auto-set to 5... \n')
                if gpu_mem in [6, 7]:
                    chunk_set = int(30)
                    widget_text.write(base_text + 'Chunk size auto-set to 30... \n')
                if gpu_mem in [8, 9, 10, 11, 12, 13, 14, 15]:
                    chunk_set = int(40)
                    widget_text.write(base_text + 'Chunk size auto-set to 40... \n')
                if int(gpu_mem) >= int(16):
                    chunk_set = int(60)
                    widget_text.write(base_text + 'Chunk size auto-set to 60... \n')
            if data['gpu'] == -1:
                sys_mem = psutil.virtual_memory().total >> 30
                if int(sys_mem) <= int(4):
                    chunk_set = int(1)
                    widget_text.write(base_text + 'Chunk size auto-set to 1... \n')
                if sys_mem in [5, 6, 7, 8]:
                    chunk_set = int(10)
                    widget_text.write(base_text + 'Chunk size auto-set to 10... \n')
                if sys_mem in [9, 10, 11, 12, 13, 14, 15, 16]:
                    chunk_set = int(25)
                    widget_text.write(base_text + 'Chunk size auto-set to 25... \n')
                if int(sys_mem) >= int(17):
                    chunk_set = int(60)
                    widget_text.write(base_text + 'Chunk size auto-set to 60... \n')
        elif data['chunks'] == 'Full':
            chunk_set = 0
            widget_text.write(base_text + "Chunk size set to full... \n")
        else:
            chunk_set = int(data['chunks'])
            widget_text.write(base_text + "Chunk size user-set to "f"{chunk_set}... \n")
            
        samples = mix.shape[-1]
        margin = margin_set
        chunk_size = chunk_set*44100
        assert not margin == 0, 'margin cannot be zero!'
        if margin > chunk_size:
            margin = chunk_size

        b = np.array([[[0.5]], [[0.5]], [[0.7]], [[0.9]]])
        segmented_mix = {}
        
        if chunk_set == 0 or samples < chunk_size:
            chunk_size = samples
        
        counter = -1
        for skip in range(0, samples, chunk_size):
            counter+=1
    
            s_margin = 0 if counter == 0 else margin
            end = min(skip+chunk_size+margin, samples)

            start = skip-s_margin

            segmented_mix[skip] = mix[:,start:end].copy()
            if end == samples:
                break
        
        if not data['demucsmodel']:
            sources = self.demix_base(segmented_mix, margin_size=margin)

        else: # both, apply spec effects
            base_out = self.demix_base(segmented_mix, margin_size=margin)
            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 = {}

            sources[3] = (spec_effects(wave=[demucs_out[3],base_out[0]],
                                        algorithm='default',
                                        value=b[3])*1.03597672895) # compensation
        return sources
    
    def demix_base(self, mixes, margin_size):
        chunked_sources = []
        onnxitera = len(mixes)
        onnxitera_calc = onnxitera * 2
        gui_progress_bar_onnx = 0
        widget_text.write(base_text + "Running ONNX Inference...\n")
        widget_text.write(base_text + "Processing "f"{onnxitera} slices... ")
        print(' Running ONNX Inference...')
        for mix in mixes:
            gui_progress_bar_onnx += 1
            if 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)))
            cmix = mixes[mix]
            sources = []
            n_sample = cmix.shape[1]
            
            mod = 0 
            for model in self.models:
                mod += 1
                trim = model.n_fft//2
                gen_size = model.chunk_size-2*trim
                pad = gen_size - n_sample%gen_size
                mix_p = np.concatenate((np.zeros((2,trim)), cmix, np.zeros((2,pad)), np.zeros((2,trim))), 1)
                mix_waves = []
                i = 0
                while i < n_sample + pad:
                    waves = np.array(mix_p[:, i:i+model.chunk_size])
                    mix_waves.append(waves)
                    i += gen_size
                mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu)
                with torch.no_grad():
                    _ort = self.onnx_models[mod]
                    spek = model.stft(mix_waves)
                    
                    tar_waves = model.istft(torch.tensor(_ort.run(None, {'input': spek.cpu().numpy()})[0]))#.cpu()

                    tar_signal = tar_waves[:,:,trim:-trim].transpose(0,1).reshape(2, -1).numpy()[:, :-pad]

                    start = 0 if mix == 0 else margin_size
                    end = None if mix == list(mixes.keys())[::-1][0] else -margin_size
                    if margin_size == 0:
                        end = None
                    sources.append(tar_signal[:,start:end])

        
            chunked_sources.append(sources)
        _sources = np.concatenate(chunked_sources, axis=-1)
        del self.onnx_models
        widget_text.write('Done!\n')
        return _sources
    
    def demix_demucs(self, mix, margin_size):
        processed = {}
        demucsitera = len(mix)
        demucsitera_calc = demucsitera * 2
        gui_progress_bar_demucs = 0
        widget_text.write(base_text + "Running Demucs Inference...\n")
        widget_text.write(base_text + "Processing "f"{len(mix)} slices... ")
        print(' Running Demucs Inference...')
        for nmix in mix:
            gui_progress_bar_demucs += 1
            update_progress(**progress_kwargs,
                step=(0.35 + (1.05/demucsitera_calc * gui_progress_bar_demucs)))
            cmix = mix[nmix]
            cmix = torch.tensor(cmix, dtype=torch.float32)
            ref = cmix.mean(0)        
            cmix = (cmix - ref.mean()) / ref.std()
            shift_set = 0
            with torch.no_grad():
                sources = apply_model(self.demucs, cmix.to(device), split=True, overlap=overlap_set, shifts=shift_set)
            sources = (sources * ref.std() + ref.mean()).cpu().numpy()
            sources[[0,1]] = sources[[1,0]]

            start = 0 if nmix == 0 else margin_size
            end = None if nmix == list(mix.keys())[::-1][0] else -margin_size
            if margin_size == 0:
                end = None
            processed[nmix] = sources[:,:,start:end].copy()

        sources = list(processed.values())
        sources = np.concatenate(sources, axis=-1)
        widget_text.write('Done!\n')
        return sources
        
data = {
    # Paths
    'input_paths': None,
    'export_path': None,
    'saveFormat': 'Wav',
    # Processing Options
    'demucsmodel': True,
    'gpu': -1,
    'chunks': 10,
    'non_red': False,
    'noisereduc_s': 3,
    'mixing': 'default',
    'modelFolder': False,
    'voc_only': False,
    'inst_only': False,
    'break': False,
    # Choose Model
    'mdxnetModel': 'UVR-MDX-NET 1',
    'high_end_process': 'mirroring',
}
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')
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

def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress_var: tk.Variable,
         **kwargs: dict):

    global widget_text
    global gui_progress_bar
    global music_file
    global channel_set
    global margin_set
    global overlap_set
    global default_chunks
    global default_noisereduc_s
    global _basename
    global _mixture
    global progress_kwargs
    global base_text
    global model_set
    global model_set_name
    
    # Update default settings
    default_chunks = data['chunks']
    default_noisereduc_s = data['noisereduc_s']

    channel_set = int(64)
    margin_set = int(44100)
    overlap_set = float(0.5)
    
    widget_text = text_widget
    gui_progress_bar = progress_var
    
    #Error Handling
    
    onnxmissing = "[ONNXRuntimeError] : 3 : NO_SUCHFILE"
    onnxmemerror = "onnxruntime::CudaCall CUDA failure 2: out of 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"
    
    
    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)
    
    if data['mdxnetModel'] == 'UVR-MDX-NET 1':
        model_set = 'UVR_MDXNET_9703.onnx'
        model_set_name = 'UVR_MDXNET_9703'
    if data['mdxnetModel'] == 'UVR-MDX-NET 2':
        model_set = 'UVR_MDXNET_9682.onnx'
        model_set_name = 'UVR_MDXNET_9682'
    if data['mdxnetModel'] == 'UVR-MDX-NET 3':
        model_set = 'UVR_MDXNET_9662.onnx'
        model_set_name = 'UVR_MDXNET_9662'
    if data['mdxnetModel'] == 'UVR-MDX-NET Karaoke':
        model_set = 'UVR_MDXNET_KARA.onnx'
        model_set_name = 'UVR_MDXNET_Karaoke'

    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)):
        
            _mixture = f'{data["input_paths"]}'
            _basename = f'{data["export_path"]}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
                
            # -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}
            
            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
            
            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_Model/demucs_extra-3646af93_org.th'

            pred = Predictor()
            pred.prediction_setup(demucs_name=demucsmodel,
                                channels=channel_set)
            
            # 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'\nPlease 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'\nPlease do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n' + 
                            f'If the error persists, your GPU might not be supported.\n\n' + 
                            message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') 
            except:
                pass
            torch.cuda.empty_cache()
            progress_var.set(0)
            button_widget.configure(state=tk.NORMAL)  # Enable Button
            return 
        
        if sf_write_err in message:
            text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
            text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
            text_widget.write(f'\nError Received:\n\n')
            text_widget.write(f'Could not write audio file.\n')
            text_widget.write(f'This could be due to low storage on target device or a system permissions issue.\n')
            text_widget.write(f"\nFor raw error details, go to the Error Log tab in the Help Guide.\n")
            text_widget.write(f'\nIf the error persists, please contact the developers.\n\n')
            text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
            try:
                with open('errorlog.txt', 'w') as f:
                    f.write(f'Last Error Received:\n\n' +
                            f'Error Received while processing "{os.path.basename(music_file)}":\n' + 
                            f'Process Method: Ensemble Mode\n\n' +
                            f'Could not write audio file.\n' + 
                            f'This could be due to low storage on target device or a system permissions issue.\n' + 
                            f'If the error persists, please contact the developers.\n\n' + 
                            message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') 
            except:
                pass
            torch.cuda.empty_cache()
            progress_var.set(0)
            button_widget.configure(state=tk.NORMAL)  # Enable Button
            return 
        
        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("\nFor raw error details, go to the Error Log tab in the Help Guide.\n")
        text_widget.write("\n" + f'Please address the error and try again.' + "\n")
        text_widget.write(f'If this error persists, please contact the developers with the error details.\n\n')
        text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
        torch.cuda.empty_cache()
        button_widget.configure(state=tk.NORMAL)  # Enable Button
        return
    
    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))