from functools import total_ordering import importlib import os from statistics import mode from pathlib import Path import pydub import hashlib from random import randrange import subprocess import soundfile as sf import torch import numpy as np from demucs.pretrained import get_model as _gm from demucs.hdemucs import HDemucs from demucs.apply import BagOfModels, apply_model import pathlib 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 import cv2 import math import librosa import numpy as np import soundfile as sf import shutil from tqdm import tqdm from datetime import datetime from lib_v5 import dataset from lib_v5 import spec_utils from lib_v5.model_param_init import ModelParameters import torch # Command line text parsing and widget manipulation from collections import defaultdict import tkinter as tk import traceback # Error Message Recent Calls import time # Timer class Predictor(): def __init__(self): pass def prediction_setup(self): global device if data['gpu'] >= 0: device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') if data['gpu'] == -1: device = torch.device('cpu') if demucs_switch == 'on': 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') self.demucs = _gm(name=demucs_model_set, repo=path_d) self.demucs.to(device) self.demucs.eval() widget_text.write('Done!\n') if isinstance(self.demucs, BagOfModels): widget_text.write(base_text + f"Selected Demucs model is a bag of {len(self.demucs.models)} model(s).\n") self.onnx_models = {} c = 0 self.models = get_models('tdf_extra', load=False, device=cpu, stems=modeltype, n_fft_scale=n_fft_scale_set, dim_f=dim_f_set) if demucs_only == 'off': widget_text.write(base_text + 'Loading ONNX model... ') update_progress(**progress_kwargs, step=0.1) c+=1 if data['gpu'] >= 0: if torch.cuda.is_available(): run_type = ['CUDAExecutionProvider'] else: data['gpu'] = -1 widget_text.write("\n" + base_text + "No NVIDIA GPU detected. Switching to CPU... ") run_type = ['CPUExecutionProvider'] elif data['gpu'] == -1: run_type = ['CPUExecutionProvider'] if demucs_only == 'off': self.onnx_models[c] = ort.InferenceSession(os.path.join('models/MDX_Net_Models', model_set), providers=run_type) print(demucs_model_set) widget_text.write('Done!\n') elif demucs_only == 'on': print(demucs_model_set) pass def prediction(self, m): mix, samplerate = librosa.load(m, mono=False, sr=44100) if mix.ndim == 1: mix = np.asfortranarray([mix,mix]) samplerate = samplerate mix = mix.T sources = self.demix(mix.T) widget_text.write(base_text + 'Inferences complete!\n') c = -1 #Main Save Path save_path = os.path.dirname(base_name) #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(base_name)}_{ModelName_2}_{vocal_name}',) else: vocal_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(base_name)}_{ModelName_2}_{vocal_name}',) #Instrumental Path Instrumental_name = '(Instrumental)' if data['modelFolder']: Instrumental_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(base_name)}_{ModelName_2}_{Instrumental_name}',) else: Instrumental_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(base_name)}_{ModelName_2}_{Instrumental_name}',) #Non-Reduced Vocal Path 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(base_name)}_{ModelName_2}_{vocal_name}_No_Reduction',) else: non_reduced_vocal_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(base_name)}_{ModelName_2}_{vocal_name}_No_Reduction',) if os.path.isfile(non_reduced_vocal_path): file_exists_n = 'there' else: file_exists_n = 'not_there' if os.path.isfile(vocal_path): file_exists = 'there' else: file_exists = 'not_there' if demucs_only == 'on': data['noisereduc_s'] == 'None' if not data['noisereduc_s'] == 'None': c += 1 if demucs_switch == 'off': if data['inst_only'] and not data['voc_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, samplerate) 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 data['inst_only'] and not data['voc_only']: widget_text.write(base_text + 'Preparing Instrumental...') else: widget_text.write(base_text + 'Saving Vocals... ') if demucs_only == 'on': if 'UVR' in model_set_name: sf.write(vocal_path, sources[1].T, samplerate) update_progress(**progress_kwargs, step=(0.95)) widget_text.write('Done!\n') if 'extra' in model_set_name: sf.write(vocal_path, sources[3].T, samplerate) update_progress(**progress_kwargs, step=(0.95)) widget_text.write('Done!\n') else: sf.write(non_reduced_vocal_path, sources[3].T, samplerate) update_progress(**progress_kwargs, step=(0.9)) widget_text.write('Done!\n') widget_text.write(base_text + 'Performing Noise Reduction... ') reduction_sen = float(data['noisereduc_s'])/10 subprocess.call("lib_v5\\sox\\sox.exe" + ' "' + f"{str(non_reduced_vocal_path)}" + '" "' + f"{str(vocal_path)}" + '" ' + "noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}", shell=True, stdout=subprocess.PIPE, stdin=subprocess.PIPE, stderr=subprocess.PIPE) update_progress(**progress_kwargs, step=(0.95)) widget_text.write('Done!\n') else: c += 1 if demucs_switch == 'off': widget_text.write(base_text + 'Saving Vocals..') sf.write(vocal_path, sources[c].T, samplerate) update_progress(**progress_kwargs, step=(0.9)) widget_text.write('Done!\n') else: widget_text.write(base_text + 'Saving Vocals... ') if demucs_only == 'on': if 'UVR' in model_set_name: sf.write(vocal_path, sources[1].T, samplerate) if 'extra' in model_set_name: sf.write(vocal_path, sources[3].T, samplerate) else: sf.write(vocal_path, sources[3].T, samplerate) 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=(0.95)) sf.write(Instrumental_path, spec_utils.cmb_spectrogram_to_wave(-v_spec, mp), mp.param['sr']) if data['inst_only']: if file_exists == 'there': pass else: try: os.remove(vocal_path) except: pass widget_text.write('Done!\n') if data['noisereduc_s'] == 'None': pass elif 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\n') def demix(self, mix): # 1 = demucs only # 0 = onnx only if data['chunks'] == 'Full': chunk_set = 0 else: chunk_set = data['chunks'] if data['chunks'] == 'Auto': if data['gpu'] == 0: try: gpu_mem = round(torch.cuda.get_device_properties(0).total_memory/1.074e+9) except: widget_text.write(base_text + 'NVIDIA GPU Required for conversion!\n') if int(gpu_mem) <= int(6): chunk_set = int(5) widget_text.write(base_text + 'Chunk size auto-set to 5... \n') if gpu_mem in [7, 8, 9, 10, 11, 12, 13, 14, 15]: chunk_set = int(10) widget_text.write(base_text + 'Chunk size auto-set to 10... \n') if int(gpu_mem) >= int(16): chunk_set = int(40) widget_text.write(base_text + 'Chunk size auto-set to 40... \n') if data['gpu'] == -1: sys_mem = psutil.virtual_memory().total >> 30 if int(sys_mem) <= int(4): chunk_set = int(1) widget_text.write(base_text + 'Chunk size auto-set to 1... \n') if sys_mem in [5, 6, 7, 8]: chunk_set = int(10) widget_text.write(base_text + 'Chunk size auto-set to 10... \n') if sys_mem in [9, 10, 11, 12, 13, 14, 15, 16]: chunk_set = int(25) widget_text.write(base_text + 'Chunk size auto-set to 25... \n') if int(sys_mem) >= int(17): chunk_set = int(60) widget_text.write(base_text + 'Chunk size auto-set to 60... \n') elif data['chunks'] == 'Full': chunk_set = 0 widget_text.write(base_text + "Chunk size set to full... \n") else: chunk_set = int(data['chunks']) widget_text.write(base_text + "Chunk size user-set to "f"{chunk_set}... \n") samples = mix.shape[-1] margin = margin_set chunk_size = chunk_set*44100 assert not margin == 0, 'margin cannot be zero!' if margin > chunk_size: margin = chunk_size b = np.array([[[0.5]], [[0.5]], [[0.7]], [[0.9]]]) segmented_mix = {} if chunk_set == 0 or samples < chunk_size: chunk_size = samples counter = -1 for skip in range(0, samples, chunk_size): counter+=1 s_margin = 0 if counter == 0 else margin end = min(skip+chunk_size+margin, samples) start = skip-s_margin segmented_mix[skip] = mix[:,start:end].copy() if end == samples: break if demucs_switch == 'off': sources = self.demix_base(segmented_mix, margin_size=margin) elif demucs_only == 'on': if split_mode == True: sources = self.demix_demucs_split(mix) if split_mode == False: sources = self.demix_demucs(segmented_mix, margin_size=margin) else: # both, apply spec effects base_out = self.demix_base(segmented_mix, margin_size=margin) if split_mode == True: demucs_out = self.demix_demucs_split(mix) if split_mode == False: demucs_out = self.demix_demucs(segmented_mix, margin_size=margin) nan_count = np.count_nonzero(np.isnan(demucs_out)) + np.count_nonzero(np.isnan(base_out)) if nan_count > 0: print('Warning: there are {} nan values in the array(s).'.format(nan_count)) demucs_out, base_out = np.nan_to_num(demucs_out), np.nan_to_num(base_out) sources = {} if 'UVR' in demucs_model_set: sources[3] = (spec_effects(wave=[demucs_out[1],base_out[0]], algorithm=data['mixing'], value=b[3])*float(data['compensate'])) # compensation else: sources[3] = (spec_effects(wave=[demucs_out[3],base_out[0]], algorithm=data['mixing'], value=b[3])*float(data['compensate'])) # 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 demucs_switch == 'on': update_progress(**progress_kwargs, step=(0.1 + (0.5/onnxitera_calc * gui_progress_bar_onnx))) else: update_progress(**progress_kwargs, step=(0.1 + (0.9/onnxitera * gui_progress_bar_onnx))) cmix = mixes[mix] sources = [] n_sample = cmix.shape[1] mod = 0 for model in self.models: mod += 1 trim = model.n_fft//2 gen_size = model.chunk_size-2*trim pad = gen_size - n_sample%gen_size mix_p = np.concatenate((np.zeros((2,trim)), cmix, np.zeros((2,pad)), np.zeros((2,trim))), 1) mix_waves = [] i = 0 while i < n_sample + pad: waves = np.array(mix_p[:, i:i+model.chunk_size]) mix_waves.append(waves) i += gen_size mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu) with torch.no_grad(): _ort = self.onnx_models[mod] spek = model.stft(mix_waves) tar_waves = model.istft(torch.tensor(_ort.run(None, {'input': spek.cpu().numpy()})[0]))#.cpu() tar_signal = tar_waves[:,:,trim:-trim].transpose(0,1).reshape(2, -1).numpy()[:, :-pad] start = 0 if mix == 0 else margin_size end = None if mix == list(mixes.keys())[::-1][0] else -margin_size if margin_size == 0: end = None sources.append(tar_signal[:,start:end]) chunked_sources.append(sources) _sources = np.concatenate(chunked_sources, axis=-1) del self.onnx_models widget_text.write('Done!\n') return _sources def demix_demucs(self, mix, margin_size): print('shift_set ', shift_set) processed = {} demucsitera = len(mix) demucsitera_calc = demucsitera * 2 gui_progress_bar_demucs = 0 widget_text.write(base_text + "Split Mode is off. (Chunks enabled for Demucs Model)\n") widget_text.write(base_text + "Running Demucs Inference...\n") widget_text.write(base_text + "Processing "f"{len(mix)} slices... ") print('Running Demucs Inference...') for nmix in mix: gui_progress_bar_demucs += 1 update_progress(**progress_kwargs, step=(0.35 + (1.05/demucsitera_calc * gui_progress_bar_demucs))) cmix = mix[nmix] cmix = torch.tensor(cmix, dtype=torch.float32) ref = cmix.mean(0) cmix = (cmix - ref.mean()) / ref.std() with torch.no_grad(): sources = apply_model(self.demucs, cmix[None], split=split_mode, device=device, overlap=overlap_set, shifts=shift_set, progress=False)[0] sources = (sources * ref.std() + ref.mean()).cpu().numpy() sources[[0,1]] = sources[[1,0]] start = 0 if nmix == 0 else margin_size end = None if nmix == list(mix.keys())[::-1][0] else -margin_size if margin_size == 0: end = None processed[nmix] = sources[:,:,start:end].copy() sources = list(processed.values()) sources = np.concatenate(sources, axis=-1) widget_text.write('Done!\n') return sources def demix_demucs_split(self, mix): print('shift_set ', shift_set) widget_text.write(base_text + "Split Mode is on. (Chunks disabled for Demucs Model)\n") widget_text.write(base_text + "Running Demucs Inference...\n") widget_text.write(base_text + "Processing "f"{len(mix)} slices... ") print(' Running Demucs Inference...') mix = torch.tensor(mix, dtype=torch.float32) ref = mix.mean(0) mix = (mix - ref.mean()) / ref.std() with torch.no_grad(): sources = apply_model(self.demucs, mix[None], split=split_mode, device=device, overlap=overlap_set, shifts=shift_set, progress=False)[0] widget_text.write('Done!\n') sources = (sources * ref.std() + ref.mean()).cpu().numpy() sources[[0,1]] = sources[[1,0]] return sources def update_progress(progress_var, total_files, file_num, step: float = 1): """Calculate the progress for the progress widget in the GUI""" base = (100 / total_files) progress = base * (file_num - 1) progress += base * step progress_var.set(progress) def get_baseText(total_files, file_num): """Create the base text for the command widget""" text = 'File {file_num}/{total_files} '.format(file_num=file_num, total_files=total_files) return text warnings.filterwarnings("ignore") cpu = torch.device('cpu') device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') def hide_opt(): with open(os.devnull, "w") as devnull: old_stdout = sys.stdout sys.stdout = devnull try: yield finally: sys.stdout = old_stdout class VocalRemover(object): def __init__(self, data, text_widget: tk.Text): self.data = data self.text_widget = text_widget self.models = defaultdict(lambda: None) self.devices = defaultdict(lambda: None) # self.offset = model.offset def update_progress(progress_var, total_files, file_num, step: float = 1): """Calculate the progress for the progress widget in the GUI""" base = (100 / total_files) progress = base * (file_num - 1) progress += base * step progress_var.set(progress) def get_baseText(total_files, file_num): """Create the base text for the command widget""" text = 'File {file_num}/{total_files} '.format(file_num=file_num, total_files=total_files) return text def determineModelFolderName(): """ Determine the name that is used for the folder and appended to the back of the music files """ modelFolderName = '' if not data['modelFolder']: # Model Test Mode not selected return modelFolderName # -Instrumental- if os.path.isfile(data['instrumentalModel']): modelFolderName += os.path.splitext(os.path.basename(data['instrumentalModel']))[0] if modelFolderName: modelFolderName = '/' + modelFolderName return modelFolderName class VocalRemover(object): def __init__(self, data, text_widget: tk.Text): self.data = data self.text_widget = text_widget # self.offset = model.offset data = { # Paths 'input_paths': None, 'export_path': None, 'saveFormat': 'wav', 'vr_ensem': '2_HP-UVR', 'vr_ensem_a': '1_HP-UVR', 'vr_ensem_b': '2_HP-UVR', 'vr_ensem_c': 'No Model', 'vr_ensem_d': 'No Model', 'vr_ensem_e': 'No Model', 'vr_ensem_mdx_a': 'No Model', 'vr_ensem_mdx_b': 'No Model', 'vr_ensem_mdx_c': 'No Model', 'mdx_ensem': 'UVR-MDX-NET 1', 'mdx_ensem_b': 'No Model', # Processing Options 'gpu': -1, 'postprocess': True, 'tta': True, 'output_image': True, 'voc_only': False, 'inst_only': False, 'demucsmodel': True, 'chunks': 'auto', 'non_red': False, 'noisereduc_s': 3, 'ensChoose': 'Basic VR Ensemble', 'algo': 'Instrumentals (Min Spec)', #Advanced Options 'appendensem': False, 'noise_pro_select': 'Auto Select', 'overlap': 0.5, 'shifts': 0, 'margin': 44100, 'split_mode': False, 'compensate': 1.03597672895, 'demucs_only': False, 'mixing': 'Default', 'DemucsModel_MDX': 'UVR_Demucs_Model_1', # Models 'instrumentalModel': None, 'useModel': None, # Constants 'window_size': 512, 'agg': 10, 'high_end_process': 'mirroring' } default_window_size = data['window_size'] default_agg = data['agg'] default_chunks = data['chunks'] default_noisereduc_s = data['noisereduc_s'] def update_progress(progress_var, total_files, file_num, step: float = 1): """Calculate the progress for the progress widget in the GUI""" base = (100 / total_files) progress = base * (file_num - 1) progress += base * step progress_var.set(progress) def get_baseText(total_files, file_num): """Create the base text for the command widget""" text = 'File {file_num}/{total_files} '.format(file_num=file_num, total_files=total_files) return text def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress_var: tk.Variable, **kwargs: dict): global widget_text global gui_progress_bar global music_file global default_chunks global default_noisereduc_s global base_name global progress_kwargs global base_text global modeltype global model_set global model_set_name global ModelName_2 global mdx_model_hash global demucs_model_set global channel_set global margin_set global overlap_set global shift_set global noise_pro_set global n_fft_scale_set global dim_f_set global split_mode global demucs_switch global demucs_only # Update default settings default_chunks = data['chunks'] default_noisereduc_s = data['noisereduc_s'] 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" onnxmemerror2 = "onnxruntime::BFCArena::AllocateRawInternal" systemmemerr = "DefaultCPUAllocator: not enough memory" runtimeerr = "CUDNN error executing cudnnSetTensorNdDescriptor" cuda_err = "CUDA out of memory" enex_err = "local variable \'enseExport\' referenced before assignment" mod_err = "ModuleNotFoundError" file_err = "FileNotFoundError" ffmp_err = """audioread\__init__.py", line 116, in audio_open""" sf_write_err = "sf.write" try: with open('errorlog.txt', 'w') as f: f.write(f'No errors to report at this time.' + f'\n\nLast Process Method Used: Ensemble Mode' + f'\nLast Conversion Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass n_fft_scale_set=6144 dim_f_set=2048 global nn_arch_sizes global nn_architecture nn_arch_sizes = [ 31191, # default 33966, 123821, 123812, 537238, 537227 # custom ] def save_files(wav_instrument, wav_vocals): """Save output music files""" vocal_name = '(Vocals)' instrumental_name = '(Instrumental)' save_path = os.path.dirname(base_name) # Swap names if vocal model VModel="Vocal" if VModel in model_name: # Reverse names vocal_name, instrumental_name = instrumental_name, vocal_name # Save Temp File # For instrumental the instrumental is the temp file # and for vocal the instrumental is the temp file due # to reversement sf.write(f'temp.wav', wav_instrument, mp.param['sr']) # -Save files- # Instrumental if instrumental_name is not None: instrumental_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(base_name)}_{ModelName_1}_{instrumental_name}', ) if VModel in ModelName_1 and data['voc_only']: sf.write(instrumental_path, wav_instrument, mp.param['sr']) elif VModel in ModelName_1 and data['inst_only']: pass elif data['voc_only']: pass else: sf.write(instrumental_path, wav_instrument, mp.param['sr']) # Vocal if vocal_name is not None: vocal_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name=f'{os.path.basename(base_name)}_{ModelName_1}_{vocal_name}', ) if VModel in ModelName_1 and data['inst_only']: sf.write(vocal_path, wav_vocals, mp.param['sr']) elif VModel in ModelName_1 and data['voc_only']: pass elif data['inst_only']: pass else: sf.write(vocal_path, wav_vocals, mp.param['sr']) data.update(kwargs) # Update default settings global default_window_size global default_agg default_window_size = data['window_size'] default_agg = data['agg'] stime = time.perf_counter() progress_var.set(0) text_widget.clear() button_widget.configure(state=tk.DISABLED) # Disable Button if os.path.exists('models/Main_Models/7_HP2-UVR.pth') \ or os.path.exists('models/Main_Models/8_HP2-UVR.pth') \ or os.path.exists('models/Main_Models/9_HP2-UVR.pth'): hp2_ens = 'on' else: hp2_ens = 'off' timestampnum = round(datetime.utcnow().timestamp()) randomnum = randrange(100000, 1000000) print('Do all of the HP models exist? ' + hp2_ens) # Separation Preperation try: #Ensemble Dictionary overlap_set = float(data['overlap']) channel_set = int(data['channel']) margin_set = int(data['margin']) shift_set = int(data['shifts']) demucs_model_set = data['DemucsModel_MDX'] split_mode = data['split_mode'] demucs_switch = data['demucsmodel'] if data['demucsmodel']: demucs_switch = 'on' else: demucs_switch = 'off' if data['demucs_only']: demucs_only = 'on' else: demucs_only = 'off' if not data['ensChoose'] == 'Manual Ensemble': #1st Model if data['vr_ensem_a'] == 'MGM_MAIN_v4': vr_ensem_a = 'models/Main_Models/MGM_MAIN_v4_sr44100_hl512_nf2048.pth' vr_ensem_a_name = 'MGM_MAIN_v4' elif data['vr_ensem_a'] == 'MGM_HIGHEND_v4': vr_ensem_a = 'models/Main_Models/MGM_HIGHEND_v4_sr44100_hl1024_nf2048.pth' vr_ensem_a_name = 'MGM_HIGHEND_v4' elif data['vr_ensem_a'] == 'MGM_LOWEND_A_v4': vr_ensem_a = 'models/Main_Models/MGM_LOWEND_A_v4_sr32000_hl512_nf2048.pth' vr_ensem_a_name = 'MGM_LOWEND_A_v4' elif data['vr_ensem_a'] == 'MGM_LOWEND_B_v4': vr_ensem_a = 'models/Main_Models/MGM_LOWEND_B_v4_sr33075_hl384_nf2048.pth' vr_ensem_a_name = 'MGM_LOWEND_B_v4' else: vr_ensem_a_name = data['vr_ensem_a'] vr_ensem_a = f'models/Main_Models/{vr_ensem_a_name}.pth' #2nd Model if data['vr_ensem_b'] == 'MGM_MAIN_v4': vr_ensem_b = 'models/Main_Models/MGM_MAIN_v4_sr44100_hl512_nf2048.pth' vr_ensem_b_name = 'MGM_MAIN_v4' elif data['vr_ensem_b'] == 'MGM_HIGHEND_v4': vr_ensem_b = 'models/Main_Models/MGM_HIGHEND_v4_sr44100_hl1024_nf2048.pth' vr_ensem_b_name = 'MGM_HIGHEND_v4' elif data['vr_ensem_b'] == 'MGM_LOWEND_A_v4': vr_ensem_b = 'models/Main_Models/MGM_LOWEND_A_v4_sr32000_hl512_nf2048.pth' vr_ensem_b_name = 'MGM_LOWEND_A_v4' elif data['vr_ensem_b'] == 'MGM_LOWEND_B_v4': vr_ensem_b = 'models/Main_Models/MGM_LOWEND_B_v4_sr33075_hl384_nf2048.pth' vr_ensem_b_name = 'MGM_LOWEND_B_v4' else: vr_ensem_b_name = data['vr_ensem_b'] vr_ensem_b = f'models/Main_Models/{vr_ensem_b_name}.pth' #3rd Model if data['vr_ensem_c'] == 'MGM_MAIN_v4': vr_ensem_c = 'models/Main_Models/MGM_MAIN_v4_sr44100_hl512_nf2048.pth' vr_ensem_c_name = 'MGM_MAIN_v4' elif data['vr_ensem_c'] == 'MGM_HIGHEND_v4': vr_ensem_c = 'models/Main_Models/MGM_HIGHEND_v4_sr44100_hl1024_nf2048.pth' vr_ensem_c_name = 'MGM_HIGHEND_v4' elif data['vr_ensem_c'] == 'MGM_LOWEND_A_v4': vr_ensem_c = 'models/Main_Models/MGM_LOWEND_A_v4_sr32000_hl512_nf2048.pth' vr_ensem_c_name = 'MGM_LOWEND_A_v4' elif data['vr_ensem_c'] == 'MGM_LOWEND_B_v4': vr_ensem_c = 'models/Main_Models/MGM_LOWEND_B_v4_sr33075_hl384_nf2048.pth' vr_ensem_c_name = 'MGM_LOWEND_B_v4' elif data['vr_ensem_c'] == 'No Model': vr_ensem_c = 'pass' vr_ensem_c_name = 'pass' else: vr_ensem_c_name = data['vr_ensem_c'] vr_ensem_c = f'models/Main_Models/{vr_ensem_c_name}.pth' #4th Model if data['vr_ensem_d'] == 'MGM_MAIN_v4': vr_ensem_d = 'models/Main_Models/MGM_MAIN_v4_sr44100_hl512_nf2048.pth' vr_ensem_d_name = 'MGM_MAIN_v4' elif data['vr_ensem_d'] == 'MGM_HIGHEND_v4': vr_ensem_d = 'models/Main_Models/MGM_HIGHEND_v4_sr44100_hl1024_nf2048.pth' vr_ensem_d_name = 'MGM_HIGHEND_v4' elif data['vr_ensem_d'] == 'MGM_LOWEND_A_v4': vr_ensem_d = 'models/Main_Models/MGM_LOWEND_A_v4_sr32000_hl512_nf2048.pth' vr_ensem_d_name = 'MGM_LOWEND_A_v4' elif data['vr_ensem_d'] == 'MGM_LOWEND_B_v4': vr_ensem_d = 'models/Main_Models/MGM_LOWEND_B_v4_sr33075_hl384_nf2048.pth' vr_ensem_d_name = 'MGM_LOWEND_B_v4' elif data['vr_ensem_d'] == 'No Model': vr_ensem_d = 'pass' vr_ensem_d_name = 'pass' else: vr_ensem_d_name = data['vr_ensem_d'] vr_ensem_d = f'models/Main_Models/{vr_ensem_d_name}.pth' # 5th Model if data['vr_ensem_e'] == 'MGM_MAIN_v4': vr_ensem_e = 'models/Main_Models/MGM_MAIN_v4_sr44100_hl512_nf2048.pth' vr_ensem_e_name = 'MGM_MAIN_v4' elif data['vr_ensem_e'] == 'MGM_HIGHEND_v4': vr_ensem_e = 'models/Main_Models/MGM_HIGHEND_v4_sr44100_hl1024_nf2048.pth' vr_ensem_e_name = 'MGM_HIGHEND_v4' elif data['vr_ensem_e'] == 'MGM_LOWEND_A_v4': vr_ensem_e = 'models/Main_Models/MGM_LOWEND_A_v4_sr32000_hl512_nf2048.pth' vr_ensem_e_name = 'MGM_LOWEND_A_v4' elif data['vr_ensem_e'] == 'MGM_LOWEND_B_v4': vr_ensem_e = 'models/Main_Models/MGM_LOWEND_B_v4_sr33075_hl384_nf2048.pth' vr_ensem_e_name = 'MGM_LOWEND_B_v4' elif data['vr_ensem_e'] == 'No Model': vr_ensem_e = 'pass' vr_ensem_e_name = 'pass' else: vr_ensem_e_name = data['vr_ensem_e'] vr_ensem_e = f'models/Main_Models/{vr_ensem_e_name}.pth' if data['vr_ensem_c'] == 'No Model' and data['vr_ensem_d'] == 'No Model' and data['vr_ensem_e'] == 'No Model': Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/2' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/2' } ] elif data['vr_ensem_c'] == 'No Model' and data['vr_ensem_d'] == 'No Model': Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/3' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/3' }, { 'model_name': vr_ensem_e_name, 'model_name_c':vr_ensem_e_name, 'model_location': vr_ensem_e, 'loop_name': 'Ensemble Mode - Model 3/3' } ] elif data['vr_ensem_c'] == 'No Model' and data['vr_ensem_e'] == 'No Model': Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/3' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/3' }, { 'model_name': vr_ensem_d_name, 'model_name_c':vr_ensem_d_name, 'model_location': vr_ensem_d, 'loop_name': 'Ensemble Mode - Model 3/3' } ] elif data['vr_ensem_d'] == 'No Model' and data['vr_ensem_e'] == 'No Model': Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/3' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/3' }, { 'model_name': vr_ensem_c_name, 'model_name_c':vr_ensem_c_name, 'model_location': vr_ensem_c, 'loop_name': 'Ensemble Mode - Model 3/3' } ] elif data['vr_ensem_d'] == 'No Model': Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/4' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/4' }, { 'model_name': vr_ensem_c_name, 'model_name_c':vr_ensem_c_name, 'model_location': vr_ensem_c, 'loop_name': 'Ensemble Mode - Model 3/4' }, { 'model_name': vr_ensem_e_name, 'model_name_c':vr_ensem_e_name, 'model_location': vr_ensem_e, 'loop_name': 'Ensemble Mode - Model 4/4' } ] elif data['vr_ensem_c'] == 'No Model': Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/4' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/4' }, { 'model_name': vr_ensem_d_name, 'model_name_c':vr_ensem_d_name, 'model_location': vr_ensem_d, 'loop_name': 'Ensemble Mode - Model 3/4' }, { 'model_name': vr_ensem_e_name, 'model_name_c':vr_ensem_e_name, 'model_location': vr_ensem_e, 'loop_name': 'Ensemble Mode - Model 4/4' } ] elif data['vr_ensem_e'] == 'No Model': Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/4' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/4' }, { 'model_name': vr_ensem_c_name, 'model_name_c':vr_ensem_c_name, 'model_location': vr_ensem_c, 'loop_name': 'Ensemble Mode - Model 3/4' }, { 'model_name': vr_ensem_d_name, 'model_name_c':vr_ensem_d_name, 'model_location': vr_ensem_d, 'loop_name': 'Ensemble Mode - Model 4/4' } ] else: Basic_Ensem = [ { 'model_name': vr_ensem_a_name, 'model_name_c':vr_ensem_a_name, 'model_location': vr_ensem_a, 'loop_name': 'Ensemble Mode - Model 1/5' }, { 'model_name': vr_ensem_b_name, 'model_name_c':vr_ensem_b_name, 'model_location': vr_ensem_b, 'loop_name': 'Ensemble Mode - Model 2/5' }, { 'model_name': vr_ensem_c_name, 'model_name_c':vr_ensem_c_name, 'model_location': vr_ensem_c, 'loop_name': 'Ensemble Mode - Model 3/5' }, { 'model_name': vr_ensem_d_name, 'model_name_c':vr_ensem_d_name, 'model_location': vr_ensem_d, 'loop_name': 'Ensemble Mode - Model 4/5' }, { 'model_name': vr_ensem_e_name, 'model_name_c':vr_ensem_e_name, 'model_location': vr_ensem_e, 'loop_name': 'Ensemble Mode - Model 5/5' } ] HP2_Models = [ { 'model_name':'7_HP2-UVR', 'model_name_c':'1st HP2 Model', 'model_location':'models/Main_Models/7_HP2-UVR.pth', 'loop_name': 'Ensemble Mode - Model 1/3' }, { 'model_name':'8_HP2-UVR', 'model_name_c':'2nd HP2 Model', 'model_location':'models/Main_Models/8_HP2-UVR.pth', 'loop_name': 'Ensemble Mode - Model 2/3' }, { 'model_name':'9_HP2-UVR', 'model_name_c':'3rd HP2 Model', 'model_location':'models/Main_Models/9_HP2-UVR.pth', 'loop_name': 'Ensemble Mode - Model 3/3' } ] All_HP_Models = [ { 'model_name':'7_HP2-UVR', 'model_name_c':'1st HP2 Model', 'model_location':'models/Main_Models/7_HP2-UVR.pth', 'loop_name': 'Ensemble Mode - Model 1/5' }, { 'model_name':'8_HP2-UVR', 'model_name_c':'2nd HP2 Model', 'model_location':'models/Main_Models/8_HP2-UVR.pth', 'loop_name': 'Ensemble Mode - Model 2/5' }, { 'model_name':'9_HP2-UVR', 'model_name_c':'3rd HP2 Model', 'model_location':'models/Main_Models/9_HP2-UVR.pth', 'loop_name': 'Ensemble Mode - Model 3/5' }, { 'model_name':'1_HP-UVR', 'model_name_c':'1st HP Model', 'model_location':'models/Main_Models/1_HP-UVR.pth', 'loop_name': 'Ensemble Mode - Model 4/5' }, { 'model_name':'2_HP-UVR', 'model_name_c':'2nd HP Model', 'model_location':'models/Main_Models/2_HP-UVR.pth', 'loop_name': 'Ensemble Mode - Model 5/5' } ] Vocal_Models = [ { 'model_name':'3_HP-Vocal-UVR', 'model_name_c':'1st Vocal Model', 'model_location':'models/Main_Models/3_HP-Vocal-UVR.pth', 'loop_name': 'Ensemble Mode - Model 1/2' }, { 'model_name':'4_HP-Vocal-UVR', 'model_name_c':'2nd Vocal Model', 'model_location':'models/Main_Models/4_HP-Vocal-UVR.pth', 'loop_name': 'Ensemble Mode - Model 2/2' } ] #VR Model 1 if data['vr_ensem'] == 'MGM_MAIN_v4': vr_ensem = 'models/Main_Models/MGM_MAIN_v4_sr44100_hl512_nf2048.pth' vr_ensem_name = 'MGM_MAIN_v4' elif data['vr_ensem'] == 'MGM_HIGHEND_v4': vr_ensem = 'models/Main_Models/MGM_HIGHEND_v4_sr44100_hl1024_nf2048.pth' vr_ensem_name = 'MGM_HIGHEND_v4' elif data['vr_ensem'] == 'MGM_LOWEND_A_v4': vr_ensem = 'models/Main_Models/MGM_LOWEND_A_v4_sr32000_hl512_nf2048.pth' vr_ensem_name = 'MGM_LOWEND_A_v4' elif data['vr_ensem'] == 'MGM_LOWEND_B_v4': vr_ensem = 'models/Main_Models/MGM_LOWEND_B_v4_sr33075_hl384_nf2048.pth' vr_ensem_name = 'MGM_LOWEND_B_v4' elif data['vr_ensem'] == 'No Model': vr_ensem = 'pass' vr_ensem_name = 'pass' else: vr_ensem_name = data['vr_ensem'] vr_ensem = f'models/Main_Models/{vr_ensem_name}.pth' #VR Model 2 if data['vr_ensem_mdx_a'] == 'MGM_MAIN_v4': vr_ensem_mdx_a = 'models/Main_Models/MGM_MAIN_v4_sr44100_hl512_nf2048.pth' vr_ensem_mdx_a_name = 'MGM_MAIN_v4' elif data['vr_ensem_mdx_a'] == 'MGM_HIGHEND_v4': vr_ensem_mdx_a = 'models/Main_Models/MGM_HIGHEND_v4_sr44100_hl1024_nf2048.pth' vr_ensem_mdx_a_name = 'MGM_HIGHEND_v4' elif data['vr_ensem_mdx_a'] == 'MGM_LOWEND_A_v4': vr_ensem_mdx_a = 'models/Main_Models/MGM_LOWEND_A_v4_sr32000_hl512_nf2048.pth' vr_ensem_mdx_a_name = 'MGM_LOWEND_A_v4' elif data['vr_ensem_mdx_a'] == 'MGM_LOWEND_B_v4': vr_ensem_mdx_a = 'models/Main_Models/MGM_LOWEND_B_v4_sr33075_hl384_nf2048.pth' vr_ensem_mdx_a_name = 'MGM_LOWEND_B_v4' elif data['vr_ensem_mdx_a'] == 'No Model': vr_ensem_mdx_a = 'pass' vr_ensem_mdx_a_name = 'pass' else: vr_ensem_mdx_a_name = data['vr_ensem_mdx_a'] vr_ensem_mdx_a = f'models/Main_Models/{vr_ensem_mdx_a_name}.pth' #VR Model 3 if data['vr_ensem_mdx_b'] == 'MGM_MAIN_v4': vr_ensem_mdx_b = 'models/Main_Models/MGM_MAIN_v4_sr44100_hl512_nf2048.pth' vr_ensem_mdx_b_name = 'MGM_MAIN_v4' elif data['vr_ensem_mdx_b'] == 'MGM_HIGHEND_v4': vr_ensem_mdx_b = 'models/Main_Models/MGM_HIGHEND_v4_sr44100_hl1024_nf2048.pth' vr_ensem_mdx_b_name = 'MGM_HIGHEND_v4' elif data['vr_ensem_mdx_b'] == 'MGM_LOWEND_A_v4': vr_ensem_mdx_b = 'models/Main_Models/MGM_LOWEND_A_v4_sr32000_hl512_nf2048.pth' vr_ensem_mdx_b_name = 'MGM_LOWEND_A_v4' elif data['vr_ensem_mdx_b'] == 'MGM_LOWEND_B_v4': vr_ensem_mdx_b = 'models/Main_Models/MGM_LOWEND_B_v4_sr33075_hl384_nf2048.pth' vr_ensem_mdx_b_name = 'MGM_LOWEND_B_v4' elif data['vr_ensem_mdx_b'] == 'No Model': vr_ensem_mdx_b = 'pass' vr_ensem_mdx_b_name = 'pass' else: vr_ensem_mdx_b_name = data['vr_ensem_mdx_b'] vr_ensem_mdx_b = f'models/Main_Models/{vr_ensem_mdx_b_name}.pth' #VR Model 4 if data['vr_ensem_mdx_c'] == 'MGM_MAIN_v4': vr_ensem_mdx_c = 'models/Main_Models/MGM_MAIN_v4_sr44100_hl512_nf2048.pth' vr_ensem_mdx_c_name = 'MGM_MAIN_v4' elif data['vr_ensem_mdx_c'] == 'MGM_HIGHEND_v4': vr_ensem_mdx_c = 'models/Main_Models/MGM_HIGHEND_v4_sr44100_hl1024_nf2048.pth' vr_ensem_mdx_c_name = 'MGM_HIGHEND_v4' elif data['vr_ensem_mdx_c'] == 'MGM_LOWEND_A_v4': vr_ensem_mdx_c = 'models/Main_Models/MGM_LOWEND_A_v4_sr32000_hl512_nf2048.pth' vr_ensem_mdx_c_name = 'MGM_LOWEND_A_v4' elif data['vr_ensem_mdx_c'] == 'MGM_LOWEND_B_v4': vr_ensem_mdx_c = 'models/Main_Models/MGM_LOWEND_B_v4_sr33075_hl384_nf2048.pth' vr_ensem_mdx_c_name = 'MGM_LOWEND_B_v4' elif data['vr_ensem_mdx_c'] == 'No Model': vr_ensem_mdx_c = 'pass' vr_ensem_mdx_c_name = 'pass' else: vr_ensem_mdx_c_name = data['vr_ensem_mdx_c'] vr_ensem_mdx_c = f'models/Main_Models/{vr_ensem_mdx_c_name}.pth' #MDX-Net Model if data['mdx_ensem'] == 'UVR-MDX-NET 1': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_1_9703.onnx'): mdx_ensem = 'UVR_MDXNET_1_9703' else: mdx_ensem = 'UVR_MDXNET_9703' if data['mdx_ensem'] == 'UVR-MDX-NET 2': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_2_9682.onnx'): mdx_ensem = 'UVR_MDXNET_2_9682' else: mdx_ensem = 'UVR_MDXNET_9682' if data['mdx_ensem'] == 'UVR-MDX-NET 3': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_3_9662.onnx'): mdx_ensem = 'UVR_MDXNET_3_9662' else: mdx_ensem = 'UVR_MDXNET_9662' if data['mdx_ensem'] == 'UVR-MDX-NET Karaoke': mdx_ensem = 'UVR_MDXNET_KARA' if data['mdx_ensem'] == 'Demucs UVR Model 1': mdx_ensem = 'UVR_Demucs_Model_1' if data['mdx_ensem'] == 'Demucs UVR Model 2': mdx_ensem = 'UVR_Demucs_Model_2' if data['mdx_ensem'] == 'Demucs mdx_extra': mdx_ensem = 'mdx_extra' if data['mdx_ensem'] == 'Demucs mdx_extra_q': mdx_ensem = 'mdx_extra_q' #MDX-Net Model 2 if data['mdx_ensem_b'] == 'UVR-MDX-NET 1': mdx_ensem_b = 'UVR_MDXNET_1_9703' if data['mdx_ensem_b'] == 'UVR-MDX-NET 2': mdx_ensem_b = 'UVR_MDXNET_2_9682' if data['mdx_ensem_b'] == 'UVR-MDX-NET 3': mdx_ensem_b = 'UVR_MDXNET_3_9662' if data['mdx_ensem_b'] == 'UVR-MDX-NET Karaoke': mdx_ensem_b = 'UVR_MDXNET_KARA' if data['mdx_ensem_b'] == 'Demucs UVR Model 1': mdx_ensem_b = 'UVR_Demucs_Model_1' if data['mdx_ensem_b'] == 'Demucs UVR Model 2': mdx_ensem_b = 'UVR_Demucs_Model_2' if data['mdx_ensem_b'] == 'Demucs mdx_extra': mdx_ensem_b = 'mdx_extra' if data['mdx_ensem_b'] == 'Demucs mdx_extra_q': mdx_ensem_b = 'mdx_extra_q' if data['mdx_ensem_b'] == 'No Model': mdx_ensem_b = 'pass' if data['vr_ensem'] == 'No Model' and data['vr_ensem_mdx_a'] == 'No Model' and data['vr_ensem_mdx_b'] == 'No Model' and data['vr_ensem_mdx_c'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': mdx_ensem, 'model_name_c': vr_ensem_name, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {mdx_ensem}', }, { 'model_name': 'pass', 'mdx_model_name': mdx_ensem_b, 'model_name_c': 'pass', 'model_location':'pass', 'loop_name': f'Ensemble Mode - Last Model - {mdx_ensem_b}', } ] elif data['vr_ensem_mdx_a'] == 'No Model' and data['vr_ensem_mdx_b'] == 'No Model' and data['vr_ensem_mdx_c'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': mdx_ensem, 'model_name_c': vr_ensem_name, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}', }, { 'model_name': 'pass', 'mdx_model_name': mdx_ensem_b, 'model_name_c': 'pass', 'model_location':'pass', 'loop_name': 'Ensemble Mode - Last Model', } ] elif data['vr_ensem_mdx_a'] == 'No Model' and data['vr_ensem_mdx_b'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': mdx_ensem_b, 'model_name_c': vr_ensem_name, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}' }, { 'model_name': vr_ensem_mdx_c_name, 'mdx_model_name': mdx_ensem, 'model_name_c': vr_ensem_mdx_c_name, 'model_location':vr_ensem_mdx_c, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_c_name}' } ] elif data['vr_ensem_mdx_a'] == 'No Model' and data['vr_ensem_mdx_c'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': mdx_ensem_b, 'model_name_c': vr_ensem_name, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}' }, { 'model_name': vr_ensem_mdx_b_name, 'mdx_model_name': mdx_ensem, 'model_name_c': vr_ensem_mdx_b_name, 'model_location':vr_ensem_mdx_b, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_b_name}' }, ] elif data['vr_ensem_mdx_b'] == 'No Model' and data['vr_ensem_mdx_c'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': mdx_ensem_b, 'model_name_c': vr_ensem_name, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}' }, { 'model_name': vr_ensem_mdx_a_name, 'mdx_model_name': mdx_ensem, 'model_name_c': vr_ensem_mdx_a_name, 'model_location':vr_ensem_mdx_a, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_a_name}' } ] elif data['vr_ensem_mdx_a'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': 'pass', 'model_name_c': vr_ensem_name, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}' }, { 'model_name': vr_ensem_mdx_b_name, 'mdx_model_name': mdx_ensem_b, 'model_name_c': vr_ensem_mdx_b_name, 'model_location':vr_ensem_mdx_b, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_b_name}' }, { 'model_name': vr_ensem_mdx_c_name, 'mdx_model_name': mdx_ensem, 'model_name_c': vr_ensem_mdx_c_name, 'model_location':vr_ensem_mdx_c, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_c_name}' } ] elif data['vr_ensem_mdx_b'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': 'pass', 'model_name_c': vr_ensem_name, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}' }, { 'model_name': vr_ensem_mdx_a_name, 'mdx_model_name': mdx_ensem_b, 'model_name_c': vr_ensem_mdx_a_name, 'model_location':vr_ensem_mdx_a, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_a_name}' }, { 'model_name': vr_ensem_mdx_c_name, 'mdx_model_name': mdx_ensem, 'model_name_c': vr_ensem_mdx_c_name, 'model_location':vr_ensem_mdx_c, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_c_name}' } ] elif data['vr_ensem_mdx_c'] == 'No Model': mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': 'pass', 'model_name_c': vr_ensem_name, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}' }, { 'model_name': vr_ensem_mdx_a_name, 'mdx_model_name': mdx_ensem_b, 'model_name_c': vr_ensem_mdx_a_name, 'model_location':vr_ensem_mdx_a, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_a_name}' }, { 'model_name': vr_ensem_mdx_b_name, 'mdx_model_name': mdx_ensem, 'model_name_c': vr_ensem_mdx_b_name, 'model_location':vr_ensem_mdx_b, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_b_name}' } ] else: mdx_vr = [ { 'model_name': vr_ensem_name, 'mdx_model_name': 'pass', 'model_name_c': vr_ensem_name, 'model_location':vr_ensem, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_name}' }, { 'model_name': vr_ensem_mdx_a_name, 'mdx_model_name': 'pass', 'model_name_c': vr_ensem_mdx_a_name, 'model_location':vr_ensem_mdx_a, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_a_name}' }, { 'model_name': vr_ensem_mdx_b_name, 'mdx_model_name': mdx_ensem_b, 'model_name_c': vr_ensem_mdx_b_name, 'model_location':vr_ensem_mdx_b, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_b_name}' }, { 'model_name': vr_ensem_mdx_c_name, 'mdx_model_name': mdx_ensem, 'model_name_c': vr_ensem_mdx_c_name, 'model_location':vr_ensem_mdx_c, 'loop_name': f'Ensemble Mode - Running Model - {vr_ensem_mdx_c_name}' } ] if data['ensChoose'] == 'Basic VR Ensemble': loops = Basic_Ensem ensefolder = 'Basic_Ensemble_Outputs' if data['vr_ensem_c'] == 'No Model' and data['vr_ensem_d'] == 'No Model' and data['vr_ensem_e'] == 'No Model': ensemode = 'Basic_Ensemble' + '_' + vr_ensem_a_name + '_' + vr_ensem_b_name elif data['vr_ensem_c'] == 'No Model' and data['vr_ensem_d'] == 'No Model': ensemode = 'Basic_Ensemble' + '_' + vr_ensem_a_name + '_' + vr_ensem_b_name + '_' + vr_ensem_e_name elif data['vr_ensem_c'] == 'No Model' and data['vr_ensem_e'] == 'No Model': ensemode = 'Basic_Ensemble' + '_' + vr_ensem_a_name + '_' + vr_ensem_b_name + '_' + vr_ensem_d_name elif data['vr_ensem_d'] == 'No Model' and data['vr_ensem_e'] == 'No Model': ensemode = 'Basic_Ensemble' + '_' + vr_ensem_a_name + '_' + vr_ensem_b_name + '_' + vr_ensem_c_name elif data['vr_ensem_c'] == 'No Model': ensemode = 'Basic_Ensemble' + '_' + vr_ensem_a_name + '_' + vr_ensem_b_name + '_' + vr_ensem_d_name + '_' + vr_ensem_e_name elif data['vr_ensem_d'] == 'No Model': ensemode = 'Basic_Ensemble' + '_' + vr_ensem_a_name + '_' + vr_ensem_b_name + '_' + vr_ensem_c_name + '_' + vr_ensem_e_name elif data['vr_ensem_e'] == 'No Model': ensemode = 'Basic_Ensemble' + '_' + vr_ensem_a_name + '_' + vr_ensem_b_name + '_' + vr_ensem_c_name + '_' + vr_ensem_d_name else: ensemode = 'Basic_Ensemble' + '_' + vr_ensem_a_name + '_' + vr_ensem_b_name + '_' + vr_ensem_c_name + '_' + vr_ensem_d_name + '_' + vr_ensem_e_name if data['ensChoose'] == 'HP2 Models': loops = HP2_Models ensefolder = 'HP2_Models_Ensemble_Outputs' ensemode = 'HP2_Models' if data['ensChoose'] == 'All HP/HP2 Models': loops = All_HP_Models ensefolder = 'All_HP_HP2_Models_Ensemble_Outputs' ensemode = 'All_HP_HP2_Models' if data['ensChoose'] == 'Vocal Models': loops = Vocal_Models ensefolder = 'Vocal_Models_Ensemble_Outputs' ensemode = 'Vocal_Models' if data['ensChoose'] == 'Multi-AI Ensemble': loops = mdx_vr ensefolder = 'MDX_VR_Ensemble_Outputs' if data['vr_ensem'] == 'No Model' and data['vr_ensem_mdx_a'] == 'No Model' and data['vr_ensem_mdx_b'] == 'No Model' and data['vr_ensem_mdx_c'] == 'No Model': ensemode = 'MDX-Net_Models' elif data['vr_ensem_mdx_a'] == 'No Model' and data['vr_ensem_mdx_b'] == 'No Model' and data['vr_ensem_mdx_c'] == 'No Model': ensemode = 'MDX-Net_' + vr_ensem_name elif data['vr_ensem_mdx_a'] == 'No Model' and data['vr_ensem_mdx_b'] == 'No Model': ensemode = 'MDX-Net_' + vr_ensem_name + '_' + vr_ensem_mdx_c_name elif data['vr_ensem_mdx_a'] == 'No Model' and data['vr_ensem_mdx_c'] == 'No Model': ensemode = 'MDX-Net_' + vr_ensem_name + '_' + vr_ensem_mdx_b_name elif data['vr_ensem_mdx_b'] == 'No Model' and data['vr_ensem_mdx_c'] == 'No Model': ensemode = 'MDX-Net_' + vr_ensem_name + '_' + vr_ensem_mdx_a_name elif data['vr_ensem_mdx_a'] == 'No Model': ensemode = 'MDX-Net_' + vr_ensem_name + '_' + vr_ensem_mdx_b_name + '_' + vr_ensem_mdx_c_name elif data['vr_ensem_mdx_b'] == 'No Model': ensemode = 'MDX-Net_' + vr_ensem_name + '_' + vr_ensem_mdx_a_name + '_' + vr_ensem_mdx_c_name elif data['vr_ensem_mdx_c'] == 'No Model': ensemode = 'MDX-Net_' + vr_ensem_name + '_' + vr_ensem_mdx_a_name + '_' + vr_ensem_mdx_b_name else: ensemode = 'MDX-Net_' + vr_ensem_name + '_' + vr_ensem_mdx_a_name + '_' + vr_ensem_mdx_b_name + '_' + vr_ensem_mdx_c_name #Prepare Audiofile(s) for file_num, music_file in enumerate(data['input_paths'], start=1): # -Get text and update progress- base_text = get_baseText(total_files=len(data['input_paths']), file_num=file_num) progress_kwargs = {'progress_var': progress_var, 'total_files': len(data['input_paths']), 'file_num': file_num} update_progress(**progress_kwargs, step=0) try: total, used, free = shutil.disk_usage("/") total_space = int(total/1.074e+9) used_space = int(used/1.074e+9) free_space = int(free/1.074e+9) if int(free/1.074e+9) <= int(2): text_widget.write('Error: Not enough storage on main drive to continue. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly. \n\nPlease ensure your main drive has at least 3 GB\'s of storage and try again.\n\n') text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n') text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n') text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n') progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if int(free/1.074e+9) in [3, 4, 5, 6, 7, 8]: text_widget.write('Warning: Your main drive is running low on storage. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly.\n\n') text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n') text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n') text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n\n') except: pass #Prepare to loop models for i, c in tqdm(enumerate(loops), disable=True, desc='Iterations..'): try: ModelName_2=(c['mdx_model_name']) except: pass if hp2_ens == 'off' and loops == HP2_Models: text_widget.write(base_text + 'You must install the UVR expansion pack in order to use this ensemble.\n') text_widget.write(base_text + 'Please install the expansion pack or choose another ensemble.\n') text_widget.write(base_text + 'See the \"Updates\" tab in the Help Guide for installation instructions.\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) return elif hp2_ens == 'off' and loops == All_HP_Models: text_widget.write(base_text + 'You must install the UVR expansion pack in order to use this ensemble.\n') text_widget.write(base_text + 'Please install the expansion pack or choose another ensemble.\n') text_widget.write(base_text + 'See the \"Updates\" tab in the Help Guide for installation instructions.\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) return def determineenseFolderName(): """ Determine the name that is used for the folder and appended to the back of the music files """ enseFolderName = '' if str(ensefolder): enseFolderName += os.path.splitext(os.path.basename(ensefolder))[0] if enseFolderName: try: enseFolderName = '/' + enseFolderName + '_' + str(timestampnum) except: enseFolderName = '/' + enseFolderName + '_' + str(randomnum) return enseFolderName enseFolderName = determineenseFolderName() if enseFolderName: folder_path = f'{data["export_path"]}{enseFolderName}' if not os.path.isdir(folder_path): os.mkdir(folder_path) # Determine File Name base_name = f'{data["export_path"]}{enseFolderName}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}' enseExport = f'{data["export_path"]}{enseFolderName}/' trackname = f'{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}' if c['model_location'] == 'pass': pass else: presentmodel = Path(c['model_location']) if presentmodel.is_file(): print(f'The file {presentmodel} exists') else: if data['ensChoose'] == 'Multi-AI Ensemble': text_widget.write(base_text + 'Model "' + c['model_name'] + '.pth" is missing.\n') text_widget.write(base_text + 'Installation of v5 Model Expansion Pack required to use this model.\n') text_widget.write(base_text + f'If the error persists, please verify all models are present.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return else: text_widget.write(base_text + 'Model "' + c['model_name'] + '.pth" is missing.\n') text_widget.write(base_text + 'Installation of v5 Model Expansion Pack required to use this model.\n\n') continue text_widget.write(c['loop_name'] + '\n\n') text_widget.write(base_text + 'Loading ' + c['model_name_c'] + '... ') aggresive_set = float(data['agg']/100) model_size = math.ceil(os.stat(c['model_location']).st_size / 1024) nn_architecture = '{}KB'.format(min(nn_arch_sizes, key=lambda x:abs(x-model_size))) nets = importlib.import_module('lib_v5.nets' + f'_{nn_architecture}'.replace('_{}KB'.format(nn_arch_sizes[0]), ''), package=None) text_widget.write('Done!\n') ModelName=(c['model_location']) #Package Models model_hash = hashlib.md5(open(ModelName,'rb').read()).hexdigest() print(model_hash) #v5 Models if model_hash == '47939caf0cfe52a0e81442b85b971dfd': model_params_d=str('lib_v5/modelparams/4band_44100.json') param_name=str('4band_44100') if model_hash == '4e4ecb9764c50a8c414fee6e10395bbe': model_params_d=str('lib_v5/modelparams/4band_v2.json') param_name=str('4band_v2') if model_hash == 'e60a1e84803ce4efc0a6551206cc4b71': model_params_d=str('lib_v5/modelparams/4band_44100.json') param_name=str('4band_44100') if model_hash == 'a82f14e75892e55e994376edbf0c8435': model_params_d=str('lib_v5/modelparams/4band_44100.json') param_name=str('4band_44100') if model_hash == '6dd9eaa6f0420af9f1d403aaafa4cc06': model_params_d=str('lib_v5/modelparams/4band_v2_sn.json') param_name=str('4band_v2_sn') if model_hash == '5c7bbca45a187e81abbbd351606164e5': model_params_d=str('lib_v5/modelparams/3band_44100_msb2.json') param_name=str('3band_44100_msb2') if model_hash == 'd6b2cb685a058a091e5e7098192d3233': model_params_d=str('lib_v5/modelparams/3band_44100_msb2.json') param_name=str('3band_44100_msb2') if model_hash == 'c1b9f38170a7c90e96f027992eb7c62b': model_params_d=str('lib_v5/modelparams/4band_44100.json') param_name=str('4band_44100') if model_hash == 'c3448ec923fa0edf3d03a19e633faa53': model_params_d=str('lib_v5/modelparams/4band_44100.json') param_name=str('4band_44100') if model_hash == '68aa2c8093d0080704b200d140f59e54': model_params_d=str('lib_v5/modelparams/3band_44100.json') param_name=str('3band_44100.json') if model_hash == 'fdc83be5b798e4bd29fe00fe6600e147': model_params_d=str('lib_v5/modelparams/3band_44100_mid.json') param_name=str('3band_44100_mid.json') if model_hash == '2ce34bc92fd57f55db16b7a4def3d745': model_params_d=str('lib_v5/modelparams/3band_44100_mid.json') param_name=str('3band_44100_mid.json') if model_hash == '52fdca89576f06cf4340b74a4730ee5f': model_params_d=str('lib_v5/modelparams/4band_44100.json') param_name=str('4band_44100.json') if model_hash == '41191165b05d38fc77f072fa9e8e8a30': model_params_d=str('lib_v5/modelparams/4band_44100.json') param_name=str('4band_44100.json') if model_hash == '89e83b511ad474592689e562d5b1f80e': model_params_d=str('lib_v5/modelparams/2band_32000.json') param_name=str('2band_32000.json') if model_hash == '0b954da81d453b716b114d6d7c95177f': model_params_d=str('lib_v5/modelparams/2band_32000.json') param_name=str('2band_32000.json') #v4 Models if model_hash == '6a00461c51c2920fd68937d4609ed6c8': model_params_d=str('lib_v5/modelparams/1band_sr16000_hl512.json') param_name=str('1band_sr16000_hl512') if model_hash == '0ab504864d20f1bd378fe9c81ef37140': model_params_d=str('lib_v5/modelparams/1band_sr32000_hl512.json') param_name=str('1band_sr32000_hl512') if model_hash == '7dd21065bf91c10f7fccb57d7d83b07f': model_params_d=str('lib_v5/modelparams/1band_sr32000_hl512.json') param_name=str('1band_sr32000_hl512') if model_hash == '80ab74d65e515caa3622728d2de07d23': model_params_d=str('lib_v5/modelparams/1band_sr32000_hl512.json') param_name=str('1band_sr32000_hl512') if model_hash == 'edc115e7fc523245062200c00caa847f': model_params_d=str('lib_v5/modelparams/1band_sr33075_hl384.json') param_name=str('1band_sr33075_hl384') if model_hash == '28063e9f6ab5b341c5f6d3c67f2045b7': model_params_d=str('lib_v5/modelparams/1band_sr33075_hl384.json') param_name=str('1band_sr33075_hl384') if model_hash == 'b58090534c52cbc3e9b5104bad666ef2': model_params_d=str('lib_v5/modelparams/1band_sr44100_hl512.json') param_name=str('1band_sr44100_hl512') if model_hash == '0cdab9947f1b0928705f518f3c78ea8f': model_params_d=str('lib_v5/modelparams/1band_sr44100_hl512.json') param_name=str('1band_sr44100_hl512') if model_hash == 'ae702fed0238afb5346db8356fe25f13': model_params_d=str('lib_v5/modelparams/1band_sr44100_hl1024.json') param_name=str('1band_sr44100_hl1024') ModelName_1=(c['model_name']) print('Model Parameters:', model_params_d) text_widget.write(base_text + 'Loading assigned model parameters ' + '\"' + param_name + '\"... ') mp = ModelParameters(model_params_d) text_widget.write('Done!\n') #Load model if os.path.isfile(c['model_location']): device = torch.device('cpu') model = nets.CascadedASPPNet(mp.param['bins'] * 2) model.load_state_dict(torch.load(c['model_location'], map_location=device)) if torch.cuda.is_available() and data['gpu'] >= 0: device = torch.device('cuda:{}'.format(data['gpu'])) model.to(device) model_name = os.path.basename(c["model_name"]) # -Go through the different steps of seperation- # Wave source text_widget.write(base_text + 'Loading audio source... ') X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {} bands_n = len(mp.param['band']) for d in range(bands_n, 0, -1): bp = mp.param['band'][d] if d == bands_n: # high-end band X_wave[d], _ = librosa.load( music_file, bp['sr'], False, dtype=np.float32, res_type=bp['res_type']) if X_wave[d].ndim == 1: X_wave[d] = np.asarray([X_wave[d], X_wave[d]]) else: # lower bands X_wave[d] = librosa.resample(X_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type']) # Stft of wave source X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']) if d == bands_n and data['high_end_process'] != 'none': input_high_end_h = (bp['n_fft']//2 - bp['crop_stop']) + (mp.param['pre_filter_stop'] - mp.param['pre_filter_start']) input_high_end = X_spec_s[d][:, bp['n_fft']//2-input_high_end_h:bp['n_fft']//2, :] text_widget.write('Done!\n') update_progress(**progress_kwargs, step=0.1) text_widget.write(base_text + 'Loading the stft of audio source... ') text_widget.write('Done!\n') text_widget.write(base_text + "Please Wait...\n") X_spec_m = spec_utils.combine_spectrograms(X_spec_s, mp) del X_wave, X_spec_s def inference(X_spec, device, model, aggressiveness): def _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness): model.eval() with torch.no_grad(): preds = [] iterations = [n_window] total_iterations = sum(iterations) text_widget.write(base_text + "Processing "f"{total_iterations} Slices... ") for i in tqdm(range(n_window)): update_progress(**progress_kwargs, step=(0.1 + (0.8/n_window * i))) start = i * roi_size X_mag_window = X_mag_pad[None, :, :, start:start + data['window_size']] X_mag_window = torch.from_numpy(X_mag_window).to(device) pred = model.predict(X_mag_window, aggressiveness) pred = pred.detach().cpu().numpy() preds.append(pred[0]) pred = np.concatenate(preds, axis=2) text_widget.write('Done!\n') return pred def preprocess(X_spec): X_mag = np.abs(X_spec) X_phase = np.angle(X_spec) return X_mag, X_phase X_mag, X_phase = preprocess(X_spec) coef = X_mag.max() X_mag_pre = X_mag / coef n_frame = X_mag_pre.shape[2] pad_l, pad_r, roi_size = dataset.make_padding(n_frame, data['window_size'], model.offset) n_window = int(np.ceil(n_frame / roi_size)) X_mag_pad = np.pad( X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant') pred = _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness) pred = pred[:, :, :n_frame] if data['tta']: pad_l += roi_size // 2 pad_r += roi_size // 2 n_window += 1 X_mag_pad = np.pad( X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant') pred_tta = _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness) pred_tta = pred_tta[:, :, roi_size // 2:] pred_tta = pred_tta[:, :, :n_frame] return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.j * X_phase) else: return pred * coef, X_mag, np.exp(1.j * X_phase) aggressiveness = {'value': aggresive_set, 'split_bin': mp.param['band'][1]['crop_stop']} if data['tta']: text_widget.write(base_text + "Running Inferences (TTA)... \n") else: text_widget.write(base_text + "Running Inference... \n") pred, X_mag, X_phase = inference(X_spec_m, device, model, aggressiveness) # update_progress(**progress_kwargs, # step=0.8) # Postprocess if data['postprocess']: try: text_widget.write(base_text + 'Post processing...') pred_inv = np.clip(X_mag - pred, 0, np.inf) pred = spec_utils.mask_silence(pred, pred_inv) text_widget.write(' Done!\n') except Exception as e: text_widget.write('\n' + base_text + 'Post process failed, check error log.\n') text_widget.write(base_text + 'Moving on...\n') traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while attempting to run Post Processing on "{os.path.basename(music_file)}":\n' + f'Process Method: Ensemble Mode\n\n' + f'If this error persists, please contact the developers.\n\n' + f'Raw error details:\n\n' + errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass # Inverse stft # nopep8 y_spec_m = pred * X_phase v_spec_m = X_spec_m - y_spec_m if data['voc_only']: pass else: text_widget.write(base_text + 'Saving Instrumental... ') if data['high_end_process'].startswith('mirroring'): input_high_end_ = spec_utils.mirroring(data['high_end_process'], y_spec_m, input_high_end, mp) wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp, input_high_end_h, input_high_end_) if data['voc_only']: pass else: text_widget.write('Done!\n') else: wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp) if data['voc_only']: pass else: text_widget.write('Done!\n') if data['inst_only']: pass else: text_widget.write(base_text + 'Saving Vocals... ') if data['high_end_process'].startswith('mirroring'): input_high_end_ = spec_utils.mirroring(data['high_end_process'], v_spec_m, input_high_end, mp) wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp, input_high_end_h, input_high_end_) if data['inst_only']: pass else: text_widget.write('Done!\n') else: wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp) if data['inst_only']: pass else: text_widget.write('Done!\n') update_progress(**progress_kwargs, step=1) # Save output music files save_files(wav_instrument, wav_vocals) # Save output image if data['output_image']: with open('{}_{}_Instruments.jpg'.format(base_name, c['model_name']), mode='wb') as f: image = spec_utils.spectrogram_to_image(y_spec_m) _, bin_image = cv2.imencode('.jpg', image) bin_image.tofile(f) with open('{}_{}_Vocals.jpg'.format(base_name, c['model_name']), mode='wb') as f: image = spec_utils.spectrogram_to_image(v_spec_m) _, bin_image = cv2.imencode('.jpg', image) bin_image.tofile(f) text_widget.write(base_text + 'Completed Seperation!\n\n') if data['ensChoose'] == 'Multi-AI Ensemble': mdx_name = c['mdx_model_name'] if c['mdx_model_name'] == 'pass': pass else: text_widget.write('Ensemble Mode - Running Model - ' + mdx_name + '\n\n') if mdx_name == 'UVR_MDXNET_1_9703': demucs_only = 'off' model_set = 'UVR_MDXNET_1_9703.onnx' model_set_name = 'UVR_MDXNET_1_9703' modeltype = 'v' demucs_model_set = data['DemucsModel_MDX'] noise_pro = 'MDX-NET_Noise_Profile_14_kHz' if mdx_name == 'UVR_MDXNET_2_9682': demucs_only = 'off' model_set = 'UVR_MDXNET_2_9682.onnx' model_set_name = 'UVR_MDXNET_2_9682' modeltype = 'v' noise_pro = 'MDX-NET_Noise_Profile_14_kHz' if mdx_name == 'UVR_MDXNET_3_9662': demucs_only = 'off' model_set = 'UVR_MDXNET_3_9662.onnx' model_set_name = 'UVR_MDXNET_3_9662' modeltype = 'v' demucs_model_set = data['DemucsModel_MDX'] noise_pro = 'MDX-NET_Noise_Profile_14_kHz' if mdx_name == 'UVR_MDXNET_KARA': demucs_only = 'off' model_set = 'UVR_MDXNET_KARA.onnx' model_set_name = 'UVR_MDXNET_KARA' modeltype = 'v' noise_pro = 'MDX-NET_Noise_Profile_14_kHz' if mdx_name == 'UVR_MDXNET_9703': demucs_only = 'off' model_set = 'UVR_MDXNET_9703.onnx' model_set_name = 'UVR_MDXNET_9703' modeltype = 'v' demucs_model_set = data['DemucsModel_MDX'] noise_pro = 'MDX-NET_Noise_Profile_14_kHz' if mdx_name == 'UVR_MDXNET_9682': demucs_only = 'off' model_set = 'UVR_MDXNET_9682.onnx' model_set_name = 'UVR_MDXNET_9682' modeltype = 'v' demucs_model_set = data['DemucsModel_MDX'] noise_pro = 'MDX-NET_Noise_Profile_14_kHz' if mdx_name == 'UVR_MDXNET_9662': demucs_only = 'off' model_set = 'UVR_MDXNET_9662.onnx' model_set_name = 'UVR_MDXNET_9662' modeltype = 'v' demucs_model_set = data['DemucsModel_MDX'] noise_pro = 'MDX-NET_Noise_Profile_14_kHz' if mdx_name == 'UVR_MDXNET_KARA': demucs_only = 'off' model_set = 'UVR_MDXNET_KARA.onnx' model_set_name = 'UVR_MDXNET_KARA' modeltype = 'v' demucs_model_set = data['DemucsModel_MDX'] noise_pro = 'MDX-NET_Noise_Profile_14_kHz' if 'Demucs' in mdx_name: demucs_only = 'on' demucs_switch = 'on' demucs_model_set = mdx_name model_set = '' model_set_name = 'UVR' modeltype = 'v' noise_pro = 'MDX-NET_Noise_Profile_14_kHz' if 'extra' in mdx_name: demucs_only = 'on' demucs_switch = 'on' demucs_model_set = mdx_name model_set = '' model_set_name = 'extra' modeltype = 'v' noise_pro = 'MDX-NET_Noise_Profile_14_kHz' print('demucs_only? ', demucs_only) if data['noise_pro_select'] == 'Auto Select': noise_pro_set = noise_pro else: noise_pro_set = data['noise_pro_select'] update_progress(**progress_kwargs, step=0) if data['noisereduc_s'] == 'None': pass else: if not os.path.isfile("lib_v5\sox\sox.exe"): data['noisereduc_s'] = 'None' data['non_red'] = False widget_text.write(base_text + 'SoX is missing and required for noise reduction.\n') widget_text.write(base_text + 'See the \"More Info\" tab in the Help Guide.\n') widget_text.write(base_text + 'Noise Reduction will be disabled until SoX is available.\n\n') e = os.path.join(data["export_path"]) pred = Predictor() pred.prediction_setup() # split pred.prediction( m=music_file, ) else: pass # Emsembling Outputs def get_files(folder="", prefix="", suffix=""): return [f"{folder}{i}" for i in os.listdir(folder) if i.startswith(prefix) if i.endswith(suffix)] if data['appendensem'] == False: voc_inst = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav"), 'output':'{}_(Instrumental)'.format(trackname), 'type': 'Instrumentals' }, { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav"), 'output': '{}_(Vocals)'.format(trackname), 'type': 'Vocals' } ] inst = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav"), 'output':'{}_(Instrumental)'.format(trackname), 'type': 'Instrumentals' } ] vocal = [ { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav"), 'output': '{}_(Vocals)'.format(trackname), 'type': 'Vocals' } ] else: voc_inst = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav"), 'output':'{}_Ensembled_{}_(Instrumental)'.format(trackname, ensemode), 'type': 'Instrumentals' }, { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav"), 'output': '{}_Ensembled_{}_(Vocals)'.format(trackname, ensemode), 'type': 'Vocals' } ] inst = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav"), 'output':'{}_Ensembled_{}_(Instrumental)'.format(trackname, ensemode), 'type': 'Instrumentals' } ] vocal = [ { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav"), 'output': '{}_Ensembled_{}_(Vocals)'.format(trackname, ensemode), 'type': 'Vocals' } ] if data['voc_only']: ensembles = vocal elif data['inst_only']: ensembles = inst else: ensembles = voc_inst try: for i, e in tqdm(enumerate(ensembles), desc="Ensembling..."): text_widget.write(base_text + "Ensembling " + e['type'] + "... ") wave, specs = {}, {} mp = ModelParameters(e['model_params']) for i in range(len(e['files'])): spec = {} for d in range(len(mp.param['band']), 0, -1): bp = mp.param['band'][d] if d == len(mp.param['band']): # high-end band wave[d], _ = librosa.load( e['files'][i], bp['sr'], False, dtype=np.float32, res_type=bp['res_type']) if len(wave[d].shape) == 1: # mono to stereo wave[d] = np.array([wave[d], wave[d]]) else: # lower bands wave[d] = librosa.resample(wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type']) spec[d] = spec_utils.wave_to_spectrogram(wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']) specs[i] = spec_utils.combine_spectrograms(spec, mp) del wave sf.write(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])), spec_utils.cmb_spectrogram_to_wave(spec_utils.ensembling(e['algorithm'], specs), mp), mp.param['sr']) if data['saveFormat'] == 'Mp3': try: musfile = pydub.AudioSegment.from_wav(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output']))) musfile.export((os.path.join('{}'.format(data['export_path']),'{}.mp3'.format(e['output']))), format="mp3", bitrate="320k") os.remove((os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])))) except Exception as e: traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' if "ffmpeg" in errmessage: text_widget.write('\n' + base_text + 'Failed to save output(s) as Mp3(s).\n') text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') text_widget.write(base_text + 'Moving on... ') else: text_widget.write('\n' + base_text + 'Failed to save output(s) as Mp3(s).\n') text_widget.write(base_text + 'Please check error log.\n') text_widget.write(base_text + 'Moving on... ') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while attempting to save file as mp3 "{os.path.basename(music_file)}".\n\n' + f'Process Method: Ensemble Mode\n\n' + f'FFmpeg might be missing or corrupted.\n\n' + f'If this error persists, please contact the developers.\n\n' + f'Raw error details:\n\n' + errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass if data['saveFormat'] == 'Flac': try: musfile = pydub.AudioSegment.from_wav(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output']))) musfile.export((os.path.join('{}'.format(data['export_path']),'{}.flac'.format(e['output']))), format="flac") os.remove((os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])))) except Exception as e: traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' if "ffmpeg" in errmessage: text_widget.write('\n' + base_text + 'Failed to save output(s) as Flac(s).\n') text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') text_widget.write(base_text + 'Moving on... ') else: text_widget.write('\n' + base_text + 'Failed to save output(s) as Flac(s).\n') text_widget.write(base_text + 'Please check error log.\n') text_widget.write(base_text + 'Moving on... ') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while attempting to save file as flac "{os.path.basename(music_file)}".\n' + f'Process Method: Ensemble Mode\n\n' + f'FFmpeg might be missing or corrupted.\n\n' + f'If this error persists, please contact the developers.\n\n' + f'Raw error details:\n\n' + errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass text_widget.write("Done!\n") except: text_widget.write('\n' + base_text + 'Not enough files to ensemble.') pass update_progress(**progress_kwargs, step=0.95) text_widget.write("\n") try: if not data['save']: # Deletes all outputs if Save All Outputs isn't checked files = get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav") for file in files: os.remove(file) if not data['save']: files = get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav") for file in files: os.remove(file) except: pass if data['save'] and data['saveFormat'] == 'Mp3': try: text_widget.write(base_text + 'Saving all ensemble outputs in Mp3... ') path = enseExport #Change working directory os.chdir(path) audio_files = os.listdir() for file in audio_files: #spliting the file into the name and the extension name, ext = os.path.splitext(file) if ext == ".wav": if trackname in file: musfile = pydub.AudioSegment.from_wav(file) #rename them using the old name + ".wav" musfile.export("{0}.mp3".format(name), format="mp3", bitrate="320k") try: files = get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav") for file in files: os.remove(file) except: pass try: files = get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav") for file in files: os.remove(file) except: pass text_widget.write('Done!\n\n') base_path = os.path.dirname(os.path.abspath(__file__)) os.chdir(base_path) except Exception as e: base_path = os.path.dirname(os.path.abspath(__file__)) os.chdir(base_path) traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' if "ffmpeg" in errmessage: text_widget.write('\n' + base_text + 'Failed to save output(s) as Mp3(s).\n') text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') text_widget.write(base_text + 'Moving on...\n') else: text_widget.write('\n' + base_text + 'Failed to save output(s) as Mp3(s).\n') text_widget.write(base_text + 'Please check error log.\n') text_widget.write(base_text + 'Moving on...\n') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'\nError Received while attempting to save ensembled outputs as mp3s.\n' + f'Process Method: Ensemble Mode\n\n' + f'FFmpeg might be missing or corrupted.\n\n' + f'If this error persists, please contact the developers.\n\n' + f'Raw error details:\n\n' + errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass if data['save'] and data['saveFormat'] == 'Flac': try: text_widget.write(base_text + 'Saving all ensemble outputs in Flac... ') path = enseExport #Change working directory os.chdir(path) audio_files = os.listdir() for file in audio_files: #spliting the file into the name and the extension name, ext = os.path.splitext(file) if ext == ".wav": if trackname in file: musfile = pydub.AudioSegment.from_wav(file) #rename them using the old name + ".wav" musfile.export("{0}.flac".format(name), format="flac") try: files = get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav") for file in files: os.remove(file) except: pass try: files = get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav") for file in files: os.remove(file) except: pass text_widget.write('Done!\n\n') base_path = os.path.dirname(os.path.abspath(__file__)) os.chdir(base_path) except Exception as e: base_path = os.path.dirname(os.path.abspath(__file__)) os.chdir(base_path) traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' if "ffmpeg" in errmessage: text_widget.write('\n' + base_text + 'Failed to save output(s) as Flac(s).\n') text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') text_widget.write(base_text + 'Moving on...\n') else: text_widget.write('\n' + base_text + 'Failed to save output(s) as Flac(s).\n') text_widget.write(base_text + 'Please check error log.\n') text_widget.write(base_text + 'Moving on...\n') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'\nError Received while attempting to ensembled outputs as Flacs.\n' + f'Process Method: Ensemble Mode\n\n' + f'FFmpeg might be missing or corrupted.\n\n' + f'If this error persists, please contact the developers.\n\n' + f'Raw error details:\n\n' + errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass try: os.remove('temp.wav') except: pass if len(os.listdir(enseExport)) == 0: #Check if the folder is empty shutil.rmtree(folder_path) #Delete folder if empty else: progress_kwargs = {'progress_var': progress_var, 'total_files': len(data['input_paths']), 'file_num': len(data['input_paths'])} base_text = get_baseText(total_files=len(data['input_paths']), file_num=len(data['input_paths'])) try: total, used, free = shutil.disk_usage("/") total_space = int(total/1.074e+9) used_space = int(used/1.074e+9) free_space = int(free/1.074e+9) if int(free/1.074e+9) <= int(2): text_widget.write('Error: Not enough storage on main drive to continue. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly. \n\nPlease ensure your main drive has at least 3 GB\'s of storage and try again.\n\n') text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n') text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n') text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n') progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if int(free/1.074e+9) in [3, 4, 5, 6, 7, 8]: text_widget.write('Warning: Your main drive is running low on storage. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly.\n\n') text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n') text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n') text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n\n') except: pass music_file = data['input_paths'] if len(data['input_paths']) <= 1: text_widget.write(base_text + "Not enough files to process.\n") pass else: update_progress(**progress_kwargs, step=0.2) savefilename = (data['input_paths'][0]) trackname1 = f'{os.path.splitext(os.path.basename(savefilename))[0]}' insts = [ { 'algorithm':'min_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output':'{}_User_Ensembled_(Min Spec)'.format(trackname1), 'type': 'Instrumentals' } ] vocals = [ { 'algorithm':'max_mag', 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output': '{}_User_Ensembled_(Max Spec)'.format(trackname1), 'type': 'Vocals' } ] invert_spec = [ { 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output': '{}_diff_si'.format(trackname1), 'type': 'Spectral Inversion' } ] invert_nor = [ { 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'output': '{}_diff_ni'.format(trackname1), 'type': 'Normal Inversion' } ] if data['algo'] == 'Instrumentals (Min Spec)': ensem = insts if data['algo'] == 'Vocals (Max Spec)': ensem = vocals if data['algo'] == 'Invert (Spectral)': ensem = invert_spec if data['algo'] == 'Invert (Normal)': ensem = invert_nor #Prepare to loop models if data['algo'] == 'Instrumentals (Min Spec)' or data['algo'] == 'Vocals (Max Spec)': for i, e in tqdm(enumerate(ensem), desc="Ensembling..."): text_widget.write(base_text + "Ensembling " + e['type'] + "... ") wave, specs = {}, {} mp = ModelParameters(e['model_params']) for i in range(len(data['input_paths'])): spec = {} for d in range(len(mp.param['band']), 0, -1): bp = mp.param['band'][d] if d == len(mp.param['band']): # high-end band wave[d], _ = librosa.load( data['input_paths'][i], bp['sr'], False, dtype=np.float32, res_type=bp['res_type']) if len(wave[d].shape) == 1: # mono to stereo wave[d] = np.array([wave[d], wave[d]]) else: # lower bands wave[d] = librosa.resample(wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type']) spec[d] = spec_utils.wave_to_spectrogram(wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']) specs[i] = spec_utils.combine_spectrograms(spec, mp) del wave sf.write(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])), spec_utils.cmb_spectrogram_to_wave(spec_utils.ensembling(e['algorithm'], specs), mp), mp.param['sr']) if data['saveFormat'] == 'Mp3': try: musfile = pydub.AudioSegment.from_wav(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output']))) musfile.export((os.path.join('{}'.format(data['export_path']),'{}.mp3'.format(e['output']))), format="mp3", bitrate="320k") os.remove((os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])))) except Exception as e: text_widget.write('\n' + base_text + 'Failed to save output(s) as Mp3.') text_widget.write('\n' + base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') text_widget.write(base_text + 'Moving on...\n') text_widget.write(base_text + f'Complete!\n') traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while attempting to run Manual Ensemble:\n' + f'Process Method: Ensemble Mode\n\n' + f'FFmpeg might be missing or corrupted.\n\n' + f'If this error persists, please contact the developers.\n\n' + f'Raw error details:\n\n' + errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass progress_var.set(0) button_widget.configure(state=tk.NORMAL) return if data['saveFormat'] == 'Flac': try: musfile = pydub.AudioSegment.from_wav(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output']))) musfile.export((os.path.join('{}'.format(data['export_path']),'{}.flac'.format(e['output']))), format="flac") os.remove((os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])))) except Exception as e: text_widget.write('\n' + base_text + 'Failed to save output as Flac.\n') text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') text_widget.write(base_text + 'Moving on...\n') text_widget.write(base_text + f'Complete!\n') traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while attempting to run Manual Ensemble:\n' + f'Process Method: Ensemble Mode\n\n' + f'FFmpeg might be missing or corrupted.\n\n' + f'If this error persists, please contact the developers.\n\n' + f'Raw error details:\n\n' + errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass progress_var.set(0) button_widget.configure(state=tk.NORMAL) return text_widget.write("Done!\n") if data['algo'] == 'Invert (Spectral)' and data['algo'] == 'Invert (Normal)': if len(data['input_paths']) != 2: text_widget.write(base_text + "Invalid file count.\n") pass else: for i, e in tqdm(enumerate(ensem), desc="Inverting..."): wave, specs = {}, {} mp = ModelParameters(e['model_params']) for i in range(len(data['input_paths'])): spec = {} for d in range(len(mp.param['band']), 0, -1): bp = mp.param['band'][d] if d == len(mp.param['band']): # high-end band wave[d], _ = librosa.load( data['input_paths'][i], bp['sr'], False, dtype=np.float32, res_type=bp['res_type']) if len(wave[d].shape) == 1: # mono to stereo wave[d] = np.array([wave[d], wave[d]]) else: # lower bands wave[d] = librosa.resample(wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type']) spec[d] = spec_utils.wave_to_spectrogram(wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']) specs[i] = spec_utils.combine_spectrograms(spec, mp) del wave ln = min([specs[0].shape[2], specs[1].shape[2]]) specs[0] = specs[0][:,:,:ln] specs[1] = specs[1][:,:,:ln] if data['algo'] == 'Invert (Spectral)': text_widget.write(base_text + "Performing " + e['type'] + "... ") X_mag = np.abs(specs[0]) y_mag = np.abs(specs[1]) max_mag = np.where(X_mag >= y_mag, X_mag, y_mag) v_spec = specs[1] - max_mag * np.exp(1.j * np.angle(specs[0])) sf.write(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])), spec_utils.cmb_spectrogram_to_wave(-v_spec, mp), mp.param['sr']) if data['algo'] == 'Invert (Normal)': v_spec = specs[0] - specs[1] sf.write(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])), spec_utils.cmb_spectrogram_to_wave(v_spec, mp), mp.param['sr']) text_widget.write("Done!\n") except Exception as e: traceback_text = ''.join(traceback.format_tb(e.__traceback__)) message = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' if runtimeerr in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'Your PC cannot process this audio file with the chunk size selected.\nPlease lower the chunk size and try again.\n\n') text_widget.write(f'If this error persists, please contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: Ensemble Mode\n\n' + f'Your PC cannot process this audio file with the chunk size selected.\nPlease lower the chunk size and try again.\n\n' + f'If this error persists, please contact the developers.\n\n' + f'Raw error details:\n\n' + message + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if cuda_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n') text_widget.write(f'Please close any GPU intensive applications and try again.\n') text_widget.write(f'If the error persists, your GPU might not be supported.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: Ensemble Mode\n\n' + f'The application was unable to allocate enough GPU memory to use this model.\n' + f'Please close any GPU intensive applications and try again.\n' + f'If the error persists, your GPU might not be supported.\n\n' + f'Raw error details:\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if mod_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'Application files(s) are missing.\n') text_widget.write("\n" + f'{type(e).__name__} - "{e}"' + "\n\n") text_widget.write(f'Please check for missing files/scripts in the app directory and try again.\n') text_widget.write(f'If the error persists, please reinstall application or contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: Ensemble Mode\n\n' + f'Application files(s) are missing.\n' + f'Please check for missing files/scripts in the app directory and try again.\n' + f'If the error persists, please reinstall application or contact the developers.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if file_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'Missing file error raised.\n') text_widget.write("\n" + f'{type(e).__name__} - "{e}"' + "\n\n") text_widget.write("\n" + f'Please address the error and try again.' + "\n") text_widget.write(f'If this error persists, please contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') torch.cuda.empty_cache() try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: Ensemble Mode\n\n' + f'Missing file error raised.\n' + "\n" + f'Please address the error and try again.' + "\n" + f'If this error persists, please contact the developers.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if ffmp_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The input file type is not supported or FFmpeg is missing.\n') text_widget.write(f'Please select a file type supported by FFmpeg and try again.\n\n') text_widget.write(f'If FFmpeg is missing or not installed, you will only be able to process \".wav\" files \nuntil it is available on this system.\n\n') text_widget.write(f'See the \"More Info\" tab in the Help Guide.\n\n') text_widget.write(f'If this error persists, please contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') torch.cuda.empty_cache() try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: Ensemble Mode\n\n' + f'The input file type is not supported or FFmpeg is missing.\nPlease select a file type supported by FFmpeg and try again.\n\n' + f'If FFmpeg is missing or not installed, you will only be able to process \".wav\" files until it is available on this system.\n\n' + f'See the \"More Info\" tab in the Help Guide.\n\n' + f'If this error persists, please contact the developers.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if onnxmissing in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The application could not detect this MDX-Net model on your system.\n') text_widget.write(f'Please make sure all the models are present in the correct directory.\n') text_widget.write(f'If the error persists, please reinstall application or contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: Ensemble Mode\n\n' + f'The application could not detect this MDX-Net model on your system.\n' + f'Please make sure all the models are present in the correct directory.\n' + f'If the error persists, please reinstall application or contact the developers.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if onnxmemerror in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n') text_widget.write(f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n') text_widget.write(f'If the error persists, your GPU might not be supported.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: Ensemble Mode\n\n' + f'The application was unable to allocate enough GPU memory to use this model.\n' + f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n' + f'If the error persists, your GPU might not be supported.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if onnxmemerror2 in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n') text_widget.write(f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n') text_widget.write(f'If the error persists, your GPU might not be supported.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: Ensemble Mode\n\n' + f'The application was unable to allocate enough GPU memory to use this model.\n' + f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n' + f'If the error persists, your GPU might not be supported.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if sf_write_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'Could not write audio file.\n') text_widget.write(f'This could be due to low storage on target device or a system permissions issue.\n') text_widget.write(f"\nFor raw error details, go to the Error Log tab in the Help Guide.\n") text_widget.write(f'\nIf the error persists, please contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: Ensemble Mode\n\n' + f'Could not write audio file.\n' + f'This could be due to low storage on target device or a system permissions issue.\n' + f'If the error persists, please contact the developers.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if systemmemerr in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The application was unable to allocate enough system memory to use this \nmodel.\n\n') text_widget.write(f'Please do the following:\n\n1. Restart this application.\n2. Ensure any CPU intensive applications are closed.\n3. Then try again.\n\n') text_widget.write(f'Please Note: Intel Pentium and Intel Celeron processors do not work well with \nthis application.\n\n') text_widget.write(f'If the error persists, the system may not have enough RAM, or your CPU might \nnot be supported.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: Ensemble Mode\n\n' + f'The application was unable to allocate enough system memory to use this model.\n' + f'Please do the following:\n\n1. Restart this application.\n2. Ensure any CPU intensive applications are closed.\n3. Then try again.\n\n' + f'Please Note: Intel Pentium and Intel Celeron processors do not work well with this application.\n\n' + f'If the error persists, the system may not have enough RAM, or your CPU might \nnot be supported.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if enex_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The application was unable to locate a model you selected for this ensemble.\n') text_widget.write(f'\nPlease do the following to use all compatible models:\n\n1. Navigate to the \"Updates\" tab in the Help Guide.\n2. Download and install the v5 Model Expansion Pack.\n3. Then try again.\n\n') text_widget.write(f'If the error persists, please verify all models are present.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: Ensemble Mode\n\n' + f'The application was unable to locate a model you selected for this ensemble.\n' + f'\nPlease do the following to use all compatible models:\n\n1. Navigate to the \"Updates\" tab in the Help Guide.\n2. Download and install the model expansion pack.\n3. Then try again.\n\n' + f'If the error persists, please verify all models are present.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return print(traceback_text) print(type(e).__name__, e) print(message) try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: Ensemble Mode\n\n' + f'If this error persists, please contact the developers with the error details.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: tk.messagebox.showerror(master=window, title='Error Details', message=message) progress_var.set(0) text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n') text_widget.write("\nFor raw error details, go to the Error Log tab in the Help Guide.\n") text_widget.write("\n" + f'Please address the error and try again.' + "\n") text_widget.write(f'If this error persists, please contact the developers with the error details.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') torch.cuda.empty_cache() button_widget.configure(state=tk.NORMAL) # Enable Button return update_progress(**progress_kwargs, step=1) print('Done!') progress_var.set(0) if not data['ensChoose'] == 'Manual Ensemble': text_widget.write(base_text + f'Conversions Completed!\n') elif data['algo'] == 'Instrumentals (Min Spec)' and len(data['input_paths']) <= 1 or data['algo'] == 'Vocals (Max Spec)' and len(data['input_paths']) <= 1: text_widget.write(base_text + f'Please select 2 or more files to use this feature and try again.\n') elif data['algo'] == 'Instrumentals (Min Spec)' or data['algo'] == 'Vocals (Max Spec)': text_widget.write(base_text + f'Ensemble Complete!\n') elif len(data['input_paths']) != 2 and data['algo'] == 'Invert (Spectral)' or len(data['input_paths']) != 2 and data['algo'] == 'Invert (Normal)': text_widget.write(base_text + f'Please select exactly 2 files to extract difference.\n') elif data['algo'] == 'Invert (Spectral)' or data['algo'] == 'Invert (Normal)': text_widget.write(base_text + f'Complete!\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') # nopep8 torch.cuda.empty_cache() button_widget.configure(state=tk.NORMAL) #Enable Button