import os import subprocess from unittest import skip from pathlib import Path import os.path from datetime import datetime import pydub import shutil import hashlib #MDX-Net #---------------------------------------- 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 from demucs.audio import AudioFile 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 #---------------------------------------- from lib_v5 import spec_utils from lib_v5.model_param_init import ModelParameters import torch # Command line text parsing and widget manipulation import tkinter as tk import traceback # Error Message Recent Calls import time # Timer from typing import Literal class Predictor(): def __init__(self): pass def prediction_setup(self): global device print('Print the gpu setting: ', data['gpu']) if data['gpu'] >= 0: device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') if data['gpu'] == -1: device = torch.device('cpu') if data['demucsmodel']: if '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 not data['demucs_only']: widget_text.write(base_text + 'Loading ONNX model... ') update_progress(**progress_kwargs, step=0.1) c+=1 if data['gpu'] >= 0: if torch.cuda.is_available(): run_type = ['CUDAExecutionProvider'] else: data['gpu'] = -1 widget_text.write("\n" + base_text + "No NVIDIA GPU detected. Switching to CPU... ") run_type = ['CPUExecutionProvider'] elif data['gpu'] == -1: run_type = ['CPUExecutionProvider'] print('Selected Model: ', model_set) self.onnx_models[c] = ort.InferenceSession(os.path.join('models/MDX_Net_Models', str(model_set) + '.onnx'), providers=run_type) if not data['demucs_only']: widget_text.write('Done!\n') def prediction(self, m): mix, samplerate = librosa.load(m, mono=False, sr=44100) print('print mix: ', mix) if mix.ndim == 1: mix = np.asfortranarray([mix,mix]) samplerate = samplerate mix = mix.T sources = self.demix(mix.T) widget_text.write(base_text + 'Inferences complete!\n') c = -1 #Main Save Path save_path = os.path.dirname(_basename) print('stemset_n: ', stemset_n) #Vocal Path if stemset_n == '(Vocals)': vocal_name = '(Vocals)' elif stemset_n == '(Other)': vocal_name = '(Other)' elif stemset_n == '(Drums)': vocal_name = '(Drums)' elif stemset_n == '(Bass)': vocal_name = '(Bass)' if data['modelFolder']: vocal_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{vocal_name}_{model_set_name}',) vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{vocal_name}_{model_set_name}',) vocal_path_flac = '{save_path}/{file_name}.flac'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{vocal_name}_{model_set_name}',) else: vocal_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{vocal_name}',) vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{vocal_name}',) vocal_path_flac = '{save_path}/{file_name}.flac'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{vocal_name}',) #Instrumental Path if stemset_n == '(Vocals)': Instrumental_name = '(Instrumental)' elif stemset_n == '(Other)': Instrumental_name = '(No_Other)' elif stemset_n == '(Drums)': Instrumental_name = '(No_Drums)' elif stemset_n == '(Bass)': Instrumental_name = '(No_Bass)' if data['modelFolder']: Instrumental_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{model_set_name}',) Instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{model_set_name}',) Instrumental_path_flac = '{save_path}/{file_name}.flac'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{model_set_name}',) else: Instrumental_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{Instrumental_name}',) Instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{Instrumental_name}',) Instrumental_path_flac = '{save_path}/{file_name}.flac'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{Instrumental_name}',) #Non-Reduced Vocal Path if stemset_n == '(Vocals)': vocal_name = '(Vocals)' elif stemset_n == '(Other)': vocal_name = '(Other)' elif stemset_n == '(Drums)': vocal_name = '(Drums)' elif stemset_n == '(Bass)': vocal_name = '(Bass)' if data['modelFolder']: non_reduced_vocal_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{vocal_name}_{model_set_name}_No_Reduction',) non_reduced_vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{vocal_name}_{model_set_name}_No_Reduction',) non_reduced_vocal_path_flac = '{save_path}/{file_name}.flac'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{vocal_name}_{model_set_name}_No_Reduction',) else: non_reduced_vocal_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{vocal_name}_No_Reduction',) non_reduced_vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{vocal_name}_No_Reduction',) non_reduced_vocal_path_flac = '{save_path}/{file_name}.flac'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{vocal_name}_No_Reduction',) if data['modelFolder']: non_reduced_Instrumental_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{model_set_name}_No_Reduction',) non_reduced_path_mp3 = '{save_path}/{file_name}.mp3'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{model_set_name}_No_Reduction',) non_reduced_Instrumental_path_flac = '{save_path}/{file_name}.flac'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_{model_set_name}_No_Reduction',) else: non_reduced_Instrumental_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_No_Reduction',) non_reduced_Instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_No_Reduction',) non_reduced_Instrumental_path_flac = '{save_path}/{file_name}.flac'.format( save_path=save_path, file_name = f'{os.path.basename(_basename)}_{Instrumental_name}_No_Reduction',) if os.path.isfile(non_reduced_vocal_path): file_exists_n = 'there' else: file_exists_n = 'not_there' if os.path.isfile(vocal_path): file_exists_v = 'there' else: file_exists_v = 'not_there' if os.path.isfile(Instrumental_path): file_exists_i = 'there' else: file_exists_i = 'not_there' print('Is there already a voc file there? ', file_exists_v) if not data['noisereduc_s'] == 'None': c += 1 if not data['demucsmodel']: if data['inst_only']: widget_text.write(base_text + 'Preparing to save Instrumental...') else: widget_text.write(base_text + 'Saving vocals... ') sf.write(non_reduced_vocal_path, sources[c].T, 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']: widget_text.write(base_text + 'Preparing Instrumental...') else: widget_text.write(base_text + 'Saving Vocals... ') if data['demucs_only']: if 'UVR' in demucs_model_set: sf.write(non_reduced_vocal_path, sources[1].T, samplerate) else: sf.write(non_reduced_vocal_path, sources[source_val].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 print(noise_pro_set) subprocess.call("lib_v5\\sox\\sox.exe" + ' "' + f"{str(non_reduced_vocal_path)}" + '" "' + f"{str(vocal_path)}" + '" ' + "noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}", shell=True, stdout=subprocess.PIPE, stdin=subprocess.PIPE, stderr=subprocess.PIPE) update_progress(**progress_kwargs, step=(0.95)) widget_text.write('Done!\n') else: c += 1 if not data['demucsmodel']: if data['inst_only']: widget_text.write(base_text + 'Preparing Instrumental...') else: widget_text.write(base_text + 'Saving Vocals... ') sf.write(vocal_path, sources[c].T, samplerate) update_progress(**progress_kwargs, step=(0.9)) widget_text.write('Done!\n') else: if data['inst_only']: widget_text.write(base_text + 'Preparing Instrumental...') else: widget_text.write(base_text + 'Saving Vocals... ') if data['demucs_only']: if 'UVR' in demucs_model_set: sf.write(vocal_path, sources[1].T, samplerate) else: sf.write(vocal_path, sources[source_val].T, samplerate) else: sf.write(vocal_path, sources[source_val].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: if not data['noisereduc_s'] == 'None': if data['nophaseinst']: finalfiles = [ { 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':[str(music_file), non_reduced_vocal_path], } ] else: finalfiles = [ { 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':[str(music_file), vocal_path], } ] else: finalfiles = [ { 'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json', 'files':[str(music_file), vocal_path], } ] widget_text.write(base_text + 'Saving Instrumental... ') for i, e in tqdm(enumerate(finalfiles)): wave, specs = {}, {} mp = ModelParameters(e['model_params']) for i in range(len(e['files'])): spec = {} for d in range(len(mp.param['band']), 0, -1): bp = mp.param['band'][d] if d == len(mp.param['band']): # high-end band wave[d], _ = librosa.load( e['files'][i], bp['sr'], False, dtype=np.float32, res_type=bp['res_type']) if len(wave[d].shape) == 1: # mono to stereo wave[d] = np.array([wave[d], wave[d]]) else: # lower bands wave[d] = librosa.resample(wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type']) spec[d] = spec_utils.wave_to_spectrogram(wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']) specs[i] = spec_utils.combine_spectrograms(spec, mp) del wave ln = min([specs[0].shape[2], specs[1].shape[2]]) specs[0] = specs[0][:,:,:ln] specs[1] = specs[1][:,:,:ln] X_mag = np.abs(specs[0]) y_mag = np.abs(specs[1]) max_mag = np.where(X_mag >= y_mag, X_mag, y_mag) v_spec = specs[1] - max_mag * np.exp(1.j * np.angle(specs[0])) update_progress(**progress_kwargs, step=(1)) if not data['noisereduc_s'] == 'None': if data['nophaseinst']: sf.write(non_reduced_Instrumental_path, spec_utils.cmb_spectrogram_to_wave(-v_spec, mp), mp.param['sr']) reduction_sen = float(data['noisereduc_s'])/10 print(noise_pro_set) subprocess.call("lib_v5\\sox\\sox.exe" + ' "' + f"{str(non_reduced_Instrumental_path)}" + '" "' + f"{str(Instrumental_path)}" + '" ' + "noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}", shell=True, stdout=subprocess.PIPE, stdin=subprocess.PIPE, stderr=subprocess.PIPE) else: sf.write(Instrumental_path, spec_utils.cmb_spectrogram_to_wave(-v_spec, mp), mp.param['sr']) else: sf.write(Instrumental_path, spec_utils.cmb_spectrogram_to_wave(-v_spec, mp), mp.param['sr']) if data['inst_only']: if file_exists_v == 'there': pass else: try: os.remove(vocal_path) except: pass widget_text.write('Done!\n') if data['saveFormat'] == 'Mp3': try: if data['inst_only'] == True: if data['non_red'] == True: if not data['nophaseinst']: pass else: musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path) musfile.export(non_reduced_Instrumental_path_mp3, format="mp3", bitrate="320k") try: os.remove(non_reduced_Instrumental_path) except: pass pass else: musfile = pydub.AudioSegment.from_wav(vocal_path) musfile.export(vocal_path_mp3, format="mp3", bitrate="320k") if file_exists_v == 'there': pass else: try: os.remove(vocal_path) except: pass if data['non_red'] == True: if not data['nophaseinst']: pass else: if data['voc_only'] == True: pass else: musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path) musfile.export(non_reduced_Instrumental_path_mp3, format="mp3", bitrate="320k") if file_exists_n == 'there': pass else: try: os.remove(non_reduced_Instrumental_path) except: pass if data['voc_only'] == True: if data['non_red'] == True: musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path) musfile.export(non_reduced_vocal_path_mp3, format="mp3", bitrate="320k") try: os.remove(non_reduced_vocal_path) except: pass pass else: musfile = pydub.AudioSegment.from_wav(Instrumental_path) musfile.export(Instrumental_path_mp3, format="mp3", bitrate="320k") if file_exists_i == 'there': pass else: try: os.remove(Instrumental_path) except: pass if data['non_red'] == True: if data['inst_only'] == True: pass else: musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path) musfile.export(non_reduced_vocal_path_mp3, format="mp3", bitrate="320k") if file_exists_n == 'there': pass else: try: os.remove(non_reduced_vocal_path) except: pass except Exception as e: traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' if "ffmpeg" in errmessage: widget_text.write(base_text + 'Failed to save output(s) as Mp3(s).\n') widget_text.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') widget_text.write(base_text + 'Moving on...\n') else: widget_text.write(base_text + 'Failed to save output(s) as Mp3(s).\n') widget_text.write(base_text + 'Please check error log.\n') widget_text.write(base_text + 'Moving on...\n') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while attempting to save file as mp3 "{os.path.basename(music_file)}":\n\n' + f'Process Method: MDX-Net\n\n' + f'FFmpeg might be missing or corrupted.\n\n' + f'If this error persists, please contact the developers.\n\n' + f'Raw error details:\n\n' + errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass if data['saveFormat'] == 'Flac': try: if data['inst_only'] == True: if data['non_red'] == True: if not data['nophaseinst']: pass else: musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path) musfile.export(non_reduced_Instrumental_path_flac, format="flac") try: os.remove(non_reduced_Instrumental_path) except: pass pass else: musfile = pydub.AudioSegment.from_wav(vocal_path) musfile.export(vocal_path_flac, format="flac") if file_exists_v == 'there': pass else: try: os.remove(vocal_path) except: pass if data['non_red'] == True: if not data['nophaseinst']: pass else: if data['voc_only'] == True: pass else: musfile = pydub.AudioSegment.from_wav(non_reduced_Instrumental_path) musfile.export(non_reduced_Instrumental_path_flac, format="flac") if file_exists_n == 'there': pass else: try: os.remove(non_reduced_Instrumental_path) except: pass if data['voc_only'] == True: if data['non_red'] == True: musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path) musfile.export(non_reduced_vocal_path_flac, format="flac") try: os.remove(non_reduced_vocal_path) except: pass pass else: musfile = pydub.AudioSegment.from_wav(Instrumental_path) musfile.export(Instrumental_path_flac, format="flac") if file_exists_i == 'there': pass else: try: os.remove(Instrumental_path) except: pass if data['non_red'] == True: if data['inst_only'] == True: pass else: musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path) musfile.export(non_reduced_vocal_path_flac, format="flac") if file_exists_n == 'there': pass else: try: os.remove(non_reduced_vocal_path) except: pass except Exception as e: traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' if "ffmpeg" in errmessage: widget_text.write(base_text + 'Failed to save output(s) as Flac(s).\n') widget_text.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') widget_text.write(base_text + 'Moving on...\n') else: widget_text.write(base_text + 'Failed to save output(s) as Flac(s).\n') widget_text.write(base_text + 'Please check error log.\n') widget_text.write(base_text + 'Moving on...\n') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while attempting to save file as flac "{os.path.basename(music_file)}":\n\n' + f'Process Method: MDX-Net\n\n' + f'FFmpeg might be missing or corrupted.\n\n' + f'If this error persists, please contact the developers.\n\n' + f'Raw error details:\n\n' + errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass if data['noisereduc_s'] == 'None': pass elif data['non_red'] == True: if data['inst_only']: if file_exists_n == 'there': pass else: try: os.remove(non_reduced_vocal_path) except: pass 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) os.remove(non_reduced_Instrumental_path) except: pass widget_text.write(base_text + 'Completed Seperation!\n') def demix(self, mix): # 1 = demucs only # 0 = onnx only if data['chunks'] == 'Full': chunk_set = 0 else: chunk_set = data['chunks'] if data['chunks'] == 'Auto': if data['gpu'] == 0: try: gpu_mem = round(torch.cuda.get_device_properties(0).total_memory/1.074e+9) except: widget_text.write(base_text + 'NVIDIA GPU Required for conversion!\n') if int(gpu_mem) <= int(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 not data['demucsmodel']: sources = self.demix_base(segmented_mix, margin_size=margin) #value=float(0.9)*float(compensate) elif data['demucs_only']: 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) print(split_mode) 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 = {} print(data['mixing']) if 'UVR' in demucs_model_set: sources[source_val] = (spec_effects(wave=[demucs_out[1],base_out[0]], algorithm=data['mixing'], value=b[source_val])*float(compensate)) # compensation else: sources[source_val] = (spec_effects(wave=[demucs_out[source_val],base_out[0]], algorithm=data['mixing'], value=b[source_val])*float(compensate)) # compensation return sources def demix_base(self, mixes, margin_size): chunked_sources = [] onnxitera = len(mixes) onnxitera_calc = onnxitera * 2 gui_progress_bar_onnx = 0 widget_text.write(base_text + "Running ONNX Inference...\n") widget_text.write(base_text + "Processing "f"{onnxitera} slices... ") print(' Running ONNX Inference...') for mix in mixes: gui_progress_bar_onnx += 1 if data['demucsmodel']: update_progress(**progress_kwargs, step=(0.1 + (0.5/onnxitera_calc * gui_progress_bar_onnx))) else: update_progress(**progress_kwargs, step=(0.1 + (0.9/onnxitera * gui_progress_bar_onnx))) cmix = mixes[mix] sources = [] n_sample = cmix.shape[1] mod = 0 for model in self.models: mod += 1 trim = model.n_fft//2 gen_size = model.chunk_size-2*trim pad = gen_size - n_sample%gen_size mix_p = np.concatenate((np.zeros((2,trim)), cmix, np.zeros((2,pad)), np.zeros((2,trim))), 1) mix_waves = [] i = 0 while i < n_sample + pad: waves = np.array(mix_p[:, i:i+model.chunk_size]) mix_waves.append(waves) i += gen_size mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu) with torch.no_grad(): _ort = self.onnx_models[mod] spek = model.stft(mix_waves) tar_waves = model.istft(torch.tensor(_ort.run(None, {'input': spek.cpu().numpy()})[0]))#.cpu() tar_signal = tar_waves[:,:,trim:-trim].transpose(0,1).reshape(2, -1).numpy()[:, :-pad] start = 0 if mix == 0 else margin_size end = None if mix == list(mixes.keys())[::-1][0] else -margin_size if margin_size == 0: end = None sources.append(tar_signal[:,start:end]) chunked_sources.append(sources) _sources = np.concatenate(chunked_sources, axis=-1) del self.onnx_models widget_text.write('Done!\n') return _sources def demix_demucs(self, mix, margin_size): 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(): print(split_mode) 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') print('the demucs model is done running') 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]] print('the demucs model is done running') return sources data = { # Paths 'input_paths': None, 'export_path': None, 'saveFormat': 'Wav', # Processing Options 'demucsmodel': True, 'gpu': -1, 'chunks': 10, 'non_red': False, 'noisereduc_s': 3, 'modelFolder': False, 'voc_only': False, 'inst_only': False, 'n_fft_scale': 6144, 'dim_f': 2048, 'noise_pro_select': 'Auto Select', 'overlap': 0.5, 'shifts': 0, 'margin': 44100, 'split_mode': False, 'nophaseinst': True, 'compensate': 1.03597672895, 'autocompensate': True, 'demucs_only': False, 'mixing': 'Default', 'DemucsModel_MDX': 'UVR_Demucs_Model_1', # Choose Model 'mdxnetModel': 'UVR-MDX-NET Main', 'mdxnetModeltype': 'Vocals (Custom)', } default_chunks = data['chunks'] default_noisereduc_s = data['noisereduc_s'] def update_progress(progress_var, total_files, file_num, step: float = 1): """Calculate the progress for the progress widget in the GUI""" base = (100 / total_files) progress = base * (file_num - 1) progress += base * step progress_var.set(progress) def get_baseText(total_files, file_num): """Create the base text for the command widget""" text = 'File {file_num}/{total_files} '.format(file_num=file_num, total_files=total_files) return text warnings.filterwarnings("ignore") cpu = torch.device('cpu') def hide_opt(): with open(os.devnull, "w") as devnull: old_stdout = sys.stdout sys.stdout = devnull try: yield finally: sys.stdout = old_stdout def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress_var: tk.Variable, **kwargs: dict): global widget_text global gui_progress_bar global music_file global default_chunks global default_noisereduc_s global _basename global _mixture global modeltype global n_fft_scale_set global dim_f_set global progress_kwargs global base_text global model_set global model_set_name global stemset_n global noise_pro_set global demucs_model_set global autocompensate global compensate global channel_set global margin_set global overlap_set global shift_set global source_val global split_mode global demucs_model_set global demucs_switch autocompensate = data['autocompensate'] # 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" mod_err = "ModuleNotFoundError" file_err = "FileNotFoundError" ffmp_err = """audioread\__init__.py", line 116, in audio_open""" sf_write_err = "sf.write" model_adv_set_err = "Got invalid dimensions for input" try: with open('errorlog.txt', 'w') as f: f.write(f'No errors to report at this time.' + f'\n\nLast Process Method Used: MDX-Net' + f'\nLast Conversion Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass data.update(kwargs) if data['mdxnetModeltype'] == 'Vocals (Custom)': stemset = 'v' source_val_set = 3 stem_name = '(Vocals)' if data['mdxnetModeltype'] == 'Other (Custom)': stemset = 'o' source_val_set = 2 stem_name = '(Other)' if data['mdxnetModeltype'] == 'Drums (Custom)': stemset = 'd' source_val_set = 1 stem_name = '(Drums)' if data['mdxnetModeltype'] == 'Bass (Custom)': stemset = 'b' source_val_set = 0 stem_name = '(Bass)' if data['mdxnetModeltype'] == 'Vocals (Default)': stemset = 'v' source_val_set = 3 stem_name = '(Vocals)' if data['mdxnetModeltype'] == 'Other (Default)': stemset = 'o' source_val_set = 2 stem_name = '(Other)' if data['mdxnetModeltype'] == 'Drums (Default)': stemset = 'd' source_val_set = 1 stem_name = '(Drums)' if data['mdxnetModeltype'] == 'Bass (Default)': stemset = 'b' source_val_set = 0 stem_name = '(Bass)' if data['mdxnetModel'] == 'UVR-MDX-NET 1': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_1_9703.onnx'): model_set = 'UVR_MDXNET_1_9703' model_set_name = 'UVR_MDXNET_1_9703' else: model_set = 'UVR_MDXNET_9703' model_set_name = 'UVR_MDXNET_9703' modeltype = 'v' noise_pro = 'MDX-NET_Noise_Profile_14_kHz' stemset_n = '(Vocals)' if autocompensate == True: compensate = 1.03597672895 else: compensate = data['compensate'] source_val = 3 n_fft_scale_set=6144 dim_f_set=2048 elif data['mdxnetModel'] == 'UVR-MDX-NET 2': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_2_9682.onnx'): model_set = 'UVR_MDXNET_2_9682' model_set_name = 'UVR_MDXNET_2_9682' else: model_set = 'UVR_MDXNET_9682' model_set_name = 'UVR_MDXNET_9682' modeltype = 'v' noise_pro = 'MDX-NET_Noise_Profile_14_kHz' stemset_n = '(Vocals)' if autocompensate == True: compensate = 1.03597672895 else: compensate = data['compensate'] source_val = 3 n_fft_scale_set=6144 dim_f_set=2048 elif data['mdxnetModel'] == 'UVR-MDX-NET 3': if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_3_9662.onnx'): model_set = 'UVR_MDXNET_3_9662' model_set_name = 'UVR_MDXNET_3_9662' else: model_set = 'UVR_MDXNET_9662' model_set_name = 'UVR_MDXNET_9662' modeltype = 'v' noise_pro = 'MDX-NET_Noise_Profile_14_kHz' stemset_n = '(Vocals)' if autocompensate == True: compensate = 1.03597672895 else: compensate = data['compensate'] source_val = 3 n_fft_scale_set=6144 dim_f_set=2048 elif data['mdxnetModel'] == 'UVR-MDX-NET Karaoke': model_set = 'UVR_MDXNET_KARA' model_set_name = 'UVR_MDXNET_Karaoke' modeltype = 'v' noise_pro = 'MDX-NET_Noise_Profile_14_kHz' stemset_n = '(Vocals)' if autocompensate == True: compensate = 1.03597672895 else: compensate = data['compensate'] source_val = 3 n_fft_scale_set=6144 dim_f_set=2048 elif data['mdxnetModel'] == 'UVR-MDX-NET Main': model_set = 'UVR_MDXNET_Main' model_set_name = 'UVR_MDXNET_Main' modeltype = 'v' noise_pro = 'MDX-NET_Noise_Profile_17_kHz' stemset_n = '(Vocals)' if autocompensate == True: compensate = 1.08 else: compensate = data['compensate'] source_val = 3 n_fft_scale_set=7680 dim_f_set=3072 elif 'other' in data['mdxnetModel']: model_set = 'other' model_set_name = 'other' modeltype = 'o' noise_pro = 'MDX-NET_Noise_Profile_Full_Band' stemset_n = '(Other)' if autocompensate == True: compensate = 1.03597672895 else: compensate = data['compensate'] source_val = 2 n_fft_scale_set=8192 dim_f_set=2048 elif 'drums' in data['mdxnetModel']: model_set = 'drums' model_set_name = 'drums' modeltype = 'd' noise_pro = 'MDX-NET_Noise_Profile_Full_Band' stemset_n = '(Drums)' if autocompensate == True: compensate = 1.03597672895 else: compensate = data['compensate'] source_val = 1 n_fft_scale_set=4096 dim_f_set=2048 elif 'bass' in data['mdxnetModel']: model_set = 'bass' model_set_name = 'bass' modeltype = 'b' noise_pro = 'MDX-NET_Noise_Profile_Full_Band' stemset_n = '(Bass)' if autocompensate == True: compensate = 1.03597672895 else: compensate = data['compensate'] source_val = 0 n_fft_scale_set=16384 dim_f_set=2048 else: model_set = data['mdxnetModel'] model_set_name = data['mdxnetModel'] modeltype = stemset noise_pro = 'MDX-NET_Noise_Profile_Full_Band' stemset_n = stem_name if autocompensate == True: compensate = 1.03597672895 else: compensate = data['compensate'] source_val = source_val_set n_fft_scale_set=int(data['n_fft_scale']) dim_f_set=int(data['dim_f']) if data['noise_pro_select'] == 'Auto Select': noise_pro_set = noise_pro else: noise_pro_set = data['noise_pro_select'] print(n_fft_scale_set) print(dim_f_set) print(data['DemucsModel_MDX']) stime = time.perf_counter() progress_var.set(0) text_widget.clear() button_widget.configure(state=tk.DISABLED) # Disable Button try: #Load File(s) for file_num, music_file in tqdm(enumerate(data['input_paths'], start=1)): overlap_set = float(data['overlap']) channel_set = int(data['channel']) margin_set = int(data['margin']) shift_set = int(data['shifts']) demucs_model_set = data['DemucsModel_MDX'] split_mode = data['split_mode'] demucs_switch = data['demucsmodel'] if stemset_n == '(Bass)': if 'UVR' in demucs_model_set: text_widget.write('The selected Demucs model can only be used with vocal stems.\n') text_widget.write('Please select a 4 stem Demucs model and try again.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return else: pass if stemset_n == '(Drums)': if 'UVR' in demucs_model_set: text_widget.write('The selected Demucs model can only be used with vocal stems.\n') text_widget.write('Please select a 4 stem Demucs model and try again.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return else: pass if stemset_n == '(Other)': if 'UVR' in demucs_model_set: text_widget.write('The selected Demucs model can only be used with vocal stems.\n') text_widget.write('Please select a 4 stem Demucs model and try again.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return else: pass _mixture = f'{data["input_paths"]}' _basename = f'{data["export_path"]}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}' # -Get text and update progress- base_text = get_baseText(total_files=len(data['input_paths']), file_num=file_num) progress_kwargs = {'progress_var': progress_var, 'total_files': len(data['input_paths']), 'file_num': file_num} print(model_set) try: if float(data['noisereduc_s']) >= 11: text_widget.write('Error: Noise Reduction only supports values between 0-10.\nPlease set a value between 0-10 (with or without decimals) and try again.') progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return total, used, free = shutil.disk_usage("/") total_space = int(total/1.074e+9) used_space = int(used/1.074e+9) free_space = int(free/1.074e+9) if int(free/1.074e+9) <= int(2): text_widget.write('Error: Not enough storage on main drive to continue. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly. \n\nPlease ensure your main drive has at least 3 GB\'s of storage and try again.\n\n') text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n') text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n') text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n') progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if int(free/1.074e+9) in [3, 4, 5, 6, 7, 8]: text_widget.write('Warning: Your main drive is running low on storage. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly.\n\n') text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n') text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n') text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n\n') except: pass if data['noisereduc_s'] == 'None': pass else: if not os.path.isfile("lib_v5\sox\sox.exe"): data['noisereduc_s'] = 'None' data['non_red'] = False widget_text.write(base_text + 'SoX is missing and required for noise reduction.\n') widget_text.write(base_text + 'See the \"More Info\" tab in the Help Guide.\n') widget_text.write(base_text + 'Noise Reduction will be disabled until SoX is available.\n\n') update_progress(**progress_kwargs, step=0) e = os.path.join(data["export_path"]) demucsmodel = 'models/Demucs_Models/' + str(data['DemucsModel_MDX']) pred = Predictor() pred.prediction_setup() print(demucsmodel) # split pred.prediction( m=music_file, ) except Exception as e: traceback_text = ''.join(traceback.format_tb(e.__traceback__)) message = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' if runtimeerr in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'Your PC cannot process this audio file with the chunk size selected.\nPlease lower the chunk size and try again.\n\n') text_widget.write(f'If this error persists, please contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: MDX-Net\n\n' + f'Your PC cannot process this audio file with the chunk size selected.\nPlease lower the chunk size and try again.\n\n' + f'If this error persists, please contact the developers.\n\n' + f'Raw error details:\n\n' + message + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if cuda_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n') text_widget.write(f'Please close any GPU intensive applications and try again.\n') text_widget.write(f'If the error persists, your GPU might not be supported.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: MDX-Net\n\n' + f'The application was unable to allocate enough GPU memory to use this model.\n' + f'Please close any GPU intensive applications and try again.\n' + f'If the error persists, your GPU might not be supported.\n\n' + f'Raw error details:\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if mod_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'Application files(s) are missing.\n') text_widget.write("\n" + f'{type(e).__name__} - "{e}"' + "\n\n") text_widget.write(f'Please check for missing files/scripts in the app directory and try again.\n') text_widget.write(f'If the error persists, please reinstall application or contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: MDX-Net\n\n' + f'Application files(s) are missing.\n' + f'Please check for missing files/scripts in the app directory and try again.\n' + f'If the error persists, please reinstall application or contact the developers.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if file_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'Missing file error raised.\n') text_widget.write("\n" + f'{type(e).__name__} - "{e}"' + "\n\n") text_widget.write("\n" + f'Please address the error and try again.' + "\n") text_widget.write(f'If this error persists, please contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') torch.cuda.empty_cache() try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: MDX-Net\n\n' + f'Missing file error raised.\n' + "\n" + f'Please address the error and try again.' + "\n" + f'If this error persists, please contact the developers.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if ffmp_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The input file type is not supported or FFmpeg is missing.\n') text_widget.write(f'Please select a file type supported by FFmpeg and try again.\n\n') text_widget.write(f'If FFmpeg is missing or not installed, you will only be able to process \".wav\" files \nuntil it is available on this system.\n\n') text_widget.write(f'See the \"More Info\" tab in the Help Guide.\n\n') text_widget.write(f'If this error persists, please contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') torch.cuda.empty_cache() try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: MDX-Net\n\n' + f'The input file type is not supported or FFmpeg is missing.\nPlease select a file type supported by FFmpeg and try again.\n\n' + f'If FFmpeg is missing or not installed, you will only be able to process \".wav\" files until it is available on this system.\n\n' + f'See the \"More Info\" tab in the Help Guide.\n\n' + f'If this error persists, please contact the developers.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if onnxmissing in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The application could not detect this MDX-Net model on your system.\n') text_widget.write(f'Please make sure all the models are present in the correct directory.\n') text_widget.write(f'If the error persists, please reinstall application or contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: MDX-Net\n\n' + f'The application could not detect this MDX-Net model on your system.\n' + f'Please make sure all the models are present in the correct directory.\n' + f'If the error persists, please reinstall application or contact the developers.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if onnxmemerror in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n') text_widget.write(f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n') text_widget.write(f'If the error persists, your GPU might not be supported.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: MDX-Net\n\n' + f'The application was unable to allocate enough GPU memory to use this model.\n' + f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n' + f'If the error persists, your GPU might not be supported.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if onnxmemerror2 in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n') text_widget.write(f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n') text_widget.write(f'If the error persists, your GPU might not be supported.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: MDX-Net\n\n' + f'The application was unable to allocate enough GPU memory to use this model.\n' + f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n' + f'If the error persists, your GPU might not be supported.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if sf_write_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'Could not write audio file.\n') text_widget.write(f'This could be due to low storage on target device or a system permissions issue.\n') text_widget.write(f"\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: MDX-Net\n\n' + f'Could not write audio file.\n' + f'This could be due to low storage on target device or a system permissions issue.\n' + f'If the error persists, please contact the developers.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if systemmemerr in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The application was unable to allocate enough system memory to use this \nmodel.\n\n') text_widget.write(f'Please do the following:\n\n1. Restart this application.\n2. Ensure any CPU intensive applications are closed.\n3. Then try again.\n\n') text_widget.write(f'Please Note: Intel Pentium and Intel Celeron processors do not work well with \nthis application.\n\n') text_widget.write(f'If the error persists, the system may not have enough RAM, or your CPU might \nnot be supported.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: MDX-Net\n\n' + f'The application was unable to allocate enough system memory to use this model.\n' + f'Please do the following:\n\n1. Restart this application.\n2. Ensure any CPU intensive applications are closed.\n3. Then try again.\n\n' + f'Please Note: Intel Pentium and Intel Celeron processors do not work well with this application.\n\n' + f'If the error persists, the system may not have enough RAM, or your CPU might \nnot be supported.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if model_adv_set_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The current ONNX model settings are not compatible with the selected \nmodel.\n\n') text_widget.write(f'Please re-configure the advanced ONNX model settings accordingly and try \nagain.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: MDX-Net\n\n' + f'The current ONNX model settings are not compatible with the selected model.\n\n' + f'Please re-configure the advanced ONNX model settings accordingly and try again.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return print(traceback_text) print(type(e).__name__, e) print(message) try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: MDX-Net\n\n' + f'If this error persists, please contact the developers with the error details.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: tk.messagebox.showerror(master=window, title='Error Details', message=message) progress_var.set(0) text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n') text_widget.write("\nFor raw error details, go to the Error Log tab in the Help Guide.\n") text_widget.write("\n" + f'Please address the error and try again.' + "\n") text_widget.write(f'If this error persists, please contact the developers with the error details.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: torch.cuda.empty_cache() except: pass button_widget.configure(state=tk.NORMAL) # Enable Button return progress_var.set(0) text_widget.write(f'\nConversion(s) Completed!\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') # nopep8 torch.cuda.empty_cache() button_widget.configure(state=tk.NORMAL) # Enable Button if __name__ == '__main__': start_time = time.time() main() print("Successfully completed music demixing.");print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))