mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-12 01:50:48 +01:00
1267 lines
71 KiB
Python
1267 lines
71 KiB
Python
import os
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import importlib
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import pydub
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import shutil
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import hashlib
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import cv2
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import librosa
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import math
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import numpy as np
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import soundfile as sf
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from tqdm import tqdm
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from demucs.pretrained import get_model as _gm
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from demucs.hdemucs import HDemucs
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from demucs.apply import BagOfModels, apply_model
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from pathlib import Path
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from models import stft, istft
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from lib_v5 import dataset
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from lib_v5 import spec_utils
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from lib_v5.model_param_init import ModelParameters
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import torch
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from datetime import datetime
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# Command line text parsing and widget manipulation
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from collections import defaultdict
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import tkinter as tk
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import traceback # Error Message Recent Calls
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import time # Timer
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from random import randrange
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class VocalRemover(object):
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def __init__(self, data, text_widget: tk.Text):
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self.data = data
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self.text_widget = text_widget
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self.models = defaultdict(lambda: None)
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self.devices = defaultdict(lambda: None)
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# self.offset = model.offset
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data = {
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# Paths
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'input_paths': None,
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'export_path': None,
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'saveFormat': 'wav',
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# Processing Options
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'gpu': -1,
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'postprocess': True,
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'tta': True,
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'output_image': True,
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'voc_only': False,
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'inst_only': False,
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# Models
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'instrumentalModel': None,
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'useModel': None,
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# Constants
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'window_size': 512,
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'agg': 10,
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'high_end_process': 'mirroring',
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'ModelParams': 'Auto',
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'demucsmodel_sel_VR': 'UVR_Demucs_Model_1',
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'overlap': 0.5,
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'shifts': 0,
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'segment': 'None',
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'split_mode': False,
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'normalize': False,
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'demucsmodelVR': True,
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'wavtype': 'PCM_16',
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'mp3bit': '320k',
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'settest': False,
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}
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default_window_size = data['window_size']
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default_agg = data['agg']
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def update_progress(progress_var, total_files, file_num, step: float = 1):
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"""Calculate the progress for the progress widget in the GUI"""
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base = (100 / total_files)
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progress = base * (file_num - 1)
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progress += base * step
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progress_var.set(progress)
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def get_baseText(total_files, file_num):
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"""Create the base text for the command widget"""
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text = 'File {file_num}/{total_files} '.format(file_num=file_num,
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total_files=total_files)
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return text
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def determineModelFolderName():
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"""
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Determine the name that is used for the folder and appended
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to the back of the music files
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"""
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modelFolderName = ''
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if not data['modelFolder']:
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# Model Test Mode not selected
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return modelFolderName
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# -Instrumental-
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if os.path.isfile(data['instrumentalModel']):
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modelFolderName += os.path.splitext(os.path.basename(data['instrumentalModel']))[0]
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if modelFolderName:
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modelFolderName = '/' + modelFolderName
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return modelFolderName
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def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress_var: tk.Variable,
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**kwargs: dict):
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global model_params_d
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global nn_arch_sizes
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global nn_architecture
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global overlap_set
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global shift_set
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global split_mode
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global demucs_model_set
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global wav_type_set
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global flac_type_set
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global mp3_bit_set
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wav_type_set = data['wavtype']
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#Error Handling
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runtimeerr = "CUDNN error executing cudnnSetTensorNdDescriptor"
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systemmemerr = "DefaultCPUAllocator: not enough memory"
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cuda_err = "CUDA out of memory"
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mod_err = "ModuleNotFoundError"
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file_err = "FileNotFoundError"
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ffmp_err = """audioread\__init__.py", line 116, in audio_open"""
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sf_write_err = "sf.write"
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try:
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with open('errorlog.txt', 'w') as f:
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f.write(f'No errors to report at this time.' + f'\n\nLast Process Method Used: VR Architecture' +
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f'\nLast Conversion Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
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except:
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pass
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nn_arch_sizes = [
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31191, # default
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33966, 123821, 123812, 537238 # custom
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]
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nn_architecture = list('{}KB'.format(s) for s in nn_arch_sizes)
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def save_files(wav_instrument, wav_vocals):
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"""Save output music files"""
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vocal_name = '(Vocals)'
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instrumental_name = '(Instrumental)'
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save_path = os.path.dirname(base_name)
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# Swap names if vocal model
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VModel="Vocal"
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if VModel in model_name:
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# Reverse names
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vocal_name, instrumental_name = instrumental_name, vocal_name
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# Save Temp File
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# For instrumental the instrumental is the temp file
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# and for vocal the instrumental is the temp file due
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# to reversement
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if data['demucsmodelVR']:
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samplerate = 44100
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else:
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samplerate = mp.param['sr']
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sf.write(f'temp.wav',
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normalization_set(wav_instrument).T, samplerate, subtype=wav_type_set)
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appendModelFolderName = modelFolderName.replace('/', '_')
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# -Save files-
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# Instrumental
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if instrumental_name is not None:
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if data['modelFolder']:
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instrumental_path = '{save_path}/{file_name}.wav'.format(
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save_path=save_path,
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file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{instrumental_name}',)
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instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format(
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save_path=save_path,
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file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{instrumental_name}',)
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instrumental_path_flac = '{save_path}/{file_name}.flac'.format(
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save_path=save_path,
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file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{instrumental_name}',)
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else:
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instrumental_path = '{save_path}/{file_name}.wav'.format(
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save_path=save_path,
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file_name=f'{os.path.basename(base_name)}_{instrumental_name}',)
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instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format(
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save_path=save_path,
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file_name=f'{os.path.basename(base_name)}_{instrumental_name}',)
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instrumental_path_flac = '{save_path}/{file_name}.flac'.format(
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save_path=save_path,
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file_name=f'{os.path.basename(base_name)}_{instrumental_name}',)
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if os.path.isfile(instrumental_path):
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file_exists_i = 'there'
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else:
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file_exists_i = 'not_there'
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if VModel in model_name and data['voc_only']:
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sf.write(instrumental_path,
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normalization_set(wav_instrument).T, samplerate, subtype=wav_type_set)
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elif VModel in model_name and data['inst_only']:
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pass
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elif data['voc_only']:
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pass
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else:
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sf.write(instrumental_path,
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normalization_set(wav_instrument).T, samplerate, subtype=wav_type_set)
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# Vocal
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if vocal_name is not None:
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if data['modelFolder']:
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vocal_path = '{save_path}/{file_name}.wav'.format(
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save_path=save_path,
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file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{vocal_name}',)
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vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format(
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save_path=save_path,
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file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{vocal_name}',)
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vocal_path_flac = '{save_path}/{file_name}.flac'.format(
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save_path=save_path,
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file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{vocal_name}',)
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else:
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vocal_path = '{save_path}/{file_name}.wav'.format(
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save_path=save_path,
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file_name=f'{os.path.basename(base_name)}_{vocal_name}',)
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vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format(
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save_path=save_path,
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file_name=f'{os.path.basename(base_name)}_{vocal_name}',)
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vocal_path_flac = '{save_path}/{file_name}.flac'.format(
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save_path=save_path,
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file_name=f'{os.path.basename(base_name)}_{vocal_name}',)
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if os.path.isfile(vocal_path):
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file_exists_v = 'there'
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else:
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file_exists_v = 'not_there'
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if VModel in model_name and data['inst_only']:
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sf.write(vocal_path,
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normalization_set(wav_vocals).T, samplerate, subtype=wav_type_set)
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elif VModel in model_name and data['voc_only']:
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pass
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elif data['inst_only']:
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pass
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else:
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sf.write(vocal_path,
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normalization_set(wav_vocals).T, samplerate, subtype=wav_type_set)
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if data['saveFormat'] == 'Mp3':
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try:
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if data['inst_only'] == True:
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pass
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else:
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musfile = pydub.AudioSegment.from_wav(vocal_path)
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musfile.export(vocal_path_mp3, format="mp3", bitrate=mp3_bit_set)
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if file_exists_v == 'there':
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pass
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else:
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try:
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os.remove(vocal_path)
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except:
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pass
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if data['voc_only'] == True:
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pass
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else:
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musfile = pydub.AudioSegment.from_wav(instrumental_path)
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musfile.export(instrumental_path_mp3, format="mp3", bitrate=mp3_bit_set)
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if file_exists_i == 'there':
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pass
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else:
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try:
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os.remove(instrumental_path)
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except:
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pass
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except Exception as e:
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traceback_text = ''.join(traceback.format_tb(e.__traceback__))
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errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n'
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if "ffmpeg" in errmessage:
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text_widget.write(base_text + 'Failed to save output(s) as Mp3(s).\n')
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text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n')
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text_widget.write(base_text + 'Moving on...\n')
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else:
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text_widget.write(base_text + 'Failed to save output(s) as Mp3(s).\n')
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text_widget.write(base_text + 'Please check error log.\n')
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text_widget.write(base_text + 'Moving on...\n')
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try:
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with open('errorlog.txt', 'w') as f:
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f.write(f'Last Error Received:\n\n' +
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f'Error Received while attempting to save file as mp3 "{os.path.basename(music_file)}":\n' +
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f'Process Method: VR Architecture\n\n' +
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f'FFmpeg might be missing or corrupted.\n\n' +
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f'If this error persists, please contact the developers.\n\n' +
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f'Raw error details:\n\n' +
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errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
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except:
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pass
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if data['saveFormat'] == 'Flac':
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try:
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if VModel in model_name:
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if data['inst_only'] == True:
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pass
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else:
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musfile = pydub.AudioSegment.from_wav(instrumental_path)
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musfile.export(instrumental_path_flac, format="flac")
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if file_exists_v == 'there':
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pass
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else:
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try:
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os.remove(instrumental_path)
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except:
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pass
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if data['voc_only'] == True:
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pass
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else:
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musfile = pydub.AudioSegment.from_wav(vocal_path)
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musfile.export(vocal_path_flac, format="flac")
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if file_exists_i == 'there':
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pass
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else:
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try:
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os.remove(vocal_path)
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except:
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pass
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else:
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if data['inst_only'] == True:
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pass
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else:
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musfile = pydub.AudioSegment.from_wav(vocal_path)
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musfile.export(vocal_path_flac, format="flac")
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if file_exists_v == 'there':
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pass
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else:
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try:
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os.remove(vocal_path)
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except:
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pass
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if data['voc_only'] == True:
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pass
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else:
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musfile = pydub.AudioSegment.from_wav(instrumental_path)
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musfile.export(instrumental_path_flac, format="flac")
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if file_exists_i == 'there':
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pass
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else:
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try:
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os.remove(instrumental_path)
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except:
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pass
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except Exception as e:
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traceback_text = ''.join(traceback.format_tb(e.__traceback__))
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errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n'
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if "ffmpeg" in errmessage:
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text_widget.write(base_text + 'Failed to save output(s) as Flac(s).\n')
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text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n')
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text_widget.write(base_text + 'Moving on...\n')
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else:
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text_widget.write(base_text + 'Failed to save output(s) as Flac(s).\n')
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text_widget.write(base_text + 'Please check error log.\n')
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text_widget.write(base_text + 'Moving on...\n')
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try:
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with open('errorlog.txt', 'w') as f:
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f.write(f'Last Error Received:\n\n' +
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f'Error Received while attempting to save file as flac "{os.path.basename(music_file)}":\n' +
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f'Process Method: VR Architecture\n\n' +
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f'FFmpeg might be missing or corrupted.\n\n' +
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f'If this error persists, please contact the developers.\n\n' +
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f'Raw error details:\n\n' +
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errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
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except:
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pass
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data.update(kwargs)
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# Update default settings
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global default_window_size
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global default_agg
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global normalization_set
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default_window_size = data['window_size']
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default_agg = data['agg']
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stime = time.perf_counter()
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progress_var.set(0)
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text_widget.clear()
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button_widget.configure(state=tk.DISABLED) # Disable Button
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overlap_set = float(data['overlap'])
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shift_set = int(data['shifts'])
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demucs_model_set = data['demucsmodel_sel_VR']
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split_mode = data['split_mode']
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if data['wavtype'] == '32-bit Float':
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wav_type_set = 'FLOAT'
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elif data['wavtype'] == '64-bit Float':
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wav_type_set = 'DOUBLE'
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else:
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wav_type_set = data['wavtype']
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flac_type_set = data['flactype']
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mp3_bit_set = data['mp3bit']
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if data['normalize'] == True:
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normalization_set = spec_utils.normalize
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print('normalization on')
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else:
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normalization_set = spec_utils.nonormalize
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print('normalization off')
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vocal_remover = VocalRemover(data, text_widget)
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modelFolderName = determineModelFolderName()
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timestampnum = round(datetime.utcnow().timestamp())
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randomnum = randrange(100000, 1000000)
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# Separation Preperation
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try: #Load File(s)
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for file_num, music_file in enumerate(data['input_paths'], start=1):
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# Determine File Name
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m=music_file
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if data['settest']:
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try:
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base_name = f'{data["export_path"]}/{str(timestampnum)}_{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
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except:
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base_name = f'{data["export_path"]}/{str(randomnum)}_{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
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else:
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base_name = f'{data["export_path"]}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
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model_name = os.path.basename(data[f'{data["useModel"]}Model'])
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model = vocal_remover.models[data['useModel']]
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device = vocal_remover.devices[data['useModel']]
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# -Get text and update progress-
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base_text = get_baseText(total_files=len(data['input_paths']),
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file_num=file_num)
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progress_kwargs = {'progress_var': progress_var,
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'total_files': len(data['input_paths']),
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'file_num': file_num}
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update_progress(**progress_kwargs,
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step=0)
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try:
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total, used, free = shutil.disk_usage("/")
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total_space = int(total/1.074e+9)
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used_space = int(used/1.074e+9)
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free_space = int(free/1.074e+9)
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if int(free/1.074e+9) <= int(2):
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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')
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text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n')
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text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n')
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text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n')
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progress_var.set(0)
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button_widget.configure(state=tk.NORMAL) # Enable Button
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return
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|
|
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['wavtype'] == '64-bit Float':
|
|
if data['saveFormat'] == 'Flac':
|
|
text_widget.write('Please select \"WAV\" as your save format to use 64-bit Float.\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
if data['wavtype'] == '64-bit Float':
|
|
if data['saveFormat'] == 'Mp3':
|
|
text_widget.write('Please select \"WAV\" as your save format to use 64-bit Float.\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
#Load Model
|
|
text_widget.write(base_text + 'Loading models...')
|
|
|
|
model_size = math.ceil(os.stat(data['instrumentalModel']).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)
|
|
|
|
aggresive_set = float(data['agg']/100)
|
|
|
|
ModelName=(data['instrumentalModel'])
|
|
|
|
#Package Models
|
|
|
|
model_hash = hashlib.md5(open(ModelName,'rb').read()).hexdigest()
|
|
print(model_hash)
|
|
|
|
#v5 Models
|
|
|
|
if model_hash == '47939caf0cfe52a0e81442b85b971dfd':
|
|
model_params_auto=str('lib_v5/modelparams/4band_44100.json')
|
|
param_name_auto=str('4band_44100')
|
|
if model_hash == '4e4ecb9764c50a8c414fee6e10395bbe':
|
|
model_params_auto=str('lib_v5/modelparams/4band_v2.json')
|
|
param_name_auto=str('4band_v2')
|
|
if model_hash == 'e60a1e84803ce4efc0a6551206cc4b71':
|
|
model_params_auto=str('lib_v5/modelparams/4band_44100.json')
|
|
param_name_auto=str('4band_44100')
|
|
if model_hash == 'a82f14e75892e55e994376edbf0c8435':
|
|
model_params_auto=str('lib_v5/modelparams/4band_44100.json')
|
|
param_name_auto=str('4band_44100')
|
|
if model_hash == '6dd9eaa6f0420af9f1d403aaafa4cc06':
|
|
model_params_auto=str('lib_v5/modelparams/4band_v2_sn.json')
|
|
param_name_auto=str('4band_v2_sn')
|
|
if model_hash == '5c7bbca45a187e81abbbd351606164e5':
|
|
model_params_auto=str('lib_v5/modelparams/3band_44100_msb2.json')
|
|
param_name_auto=str('3band_44100_msb2')
|
|
if model_hash == 'd6b2cb685a058a091e5e7098192d3233':
|
|
model_params_auto=str('lib_v5/modelparams/3band_44100_msb2.json')
|
|
param_name_auto=str('3band_44100_msb2')
|
|
if model_hash == 'c1b9f38170a7c90e96f027992eb7c62b':
|
|
model_params_auto=str('lib_v5/modelparams/4band_44100.json')
|
|
param_name_auto=str('4band_44100')
|
|
if model_hash == 'c3448ec923fa0edf3d03a19e633faa53':
|
|
model_params_auto=str('lib_v5/modelparams/4band_44100.json')
|
|
param_name_auto=str('4band_44100')
|
|
if model_hash == '68aa2c8093d0080704b200d140f59e54':
|
|
model_params_auto=str('lib_v5/modelparams/3band_44100.json')
|
|
param_name_auto=str('3band_44100.json')
|
|
if model_hash == 'fdc83be5b798e4bd29fe00fe6600e147':
|
|
model_params_auto=str('lib_v5/modelparams/3band_44100_mid.json')
|
|
param_name_auto=str('3band_44100_mid.json')
|
|
if model_hash == '2ce34bc92fd57f55db16b7a4def3d745':
|
|
model_params_auto=str('lib_v5/modelparams/3band_44100_mid.json')
|
|
param_name_auto=str('3band_44100_mid.json')
|
|
if model_hash == '52fdca89576f06cf4340b74a4730ee5f':
|
|
model_params_auto=str('lib_v5/modelparams/4band_44100.json')
|
|
param_name_auto=str('4band_44100.json')
|
|
if model_hash == '41191165b05d38fc77f072fa9e8e8a30':
|
|
model_params_auto=str('lib_v5/modelparams/4band_44100.json')
|
|
param_name_auto=str('4band_44100.json')
|
|
if model_hash == '89e83b511ad474592689e562d5b1f80e':
|
|
model_params_auto=str('lib_v5/modelparams/2band_32000.json')
|
|
param_name_auto=str('2band_32000.json')
|
|
if model_hash == '0b954da81d453b716b114d6d7c95177f':
|
|
model_params_auto=str('lib_v5/modelparams/2band_32000.json')
|
|
param_name_auto=str('2band_32000.json')
|
|
|
|
#v4 Models
|
|
|
|
if model_hash == '6a00461c51c2920fd68937d4609ed6c8':
|
|
model_params_auto=str('lib_v5/modelparams/1band_sr16000_hl512.json')
|
|
param_name_auto=str('1band_sr16000_hl512')
|
|
if model_hash == '0ab504864d20f1bd378fe9c81ef37140':
|
|
model_params_auto=str('lib_v5/modelparams/1band_sr32000_hl512.json')
|
|
param_name_auto=str('1band_sr32000_hl512')
|
|
if model_hash == '7dd21065bf91c10f7fccb57d7d83b07f':
|
|
model_params_auto=str('lib_v5/modelparams/1band_sr32000_hl512.json')
|
|
param_name_auto=str('1band_sr32000_hl512')
|
|
if model_hash == '80ab74d65e515caa3622728d2de07d23':
|
|
model_params_auto=str('lib_v5/modelparams/1band_sr32000_hl512.json')
|
|
param_name_auto=str('1band_sr32000_hl512')
|
|
if model_hash == 'edc115e7fc523245062200c00caa847f':
|
|
model_params_auto=str('lib_v5/modelparams/1band_sr33075_hl384.json')
|
|
param_name_auto=str('1band_sr33075_hl384')
|
|
if model_hash == '28063e9f6ab5b341c5f6d3c67f2045b7':
|
|
model_params_auto=str('lib_v5/modelparams/1band_sr33075_hl384.json')
|
|
param_name_auto=str('1band_sr33075_hl384')
|
|
if model_hash == 'b58090534c52cbc3e9b5104bad666ef2':
|
|
model_params_auto=str('lib_v5/modelparams/1band_sr44100_hl512.json')
|
|
param_name_auto=str('1band_sr44100_hl512')
|
|
if model_hash == '0cdab9947f1b0928705f518f3c78ea8f':
|
|
model_params_auto=str('lib_v5/modelparams/1band_sr44100_hl512.json')
|
|
param_name_auto=str('1band_sr44100_hl512')
|
|
if model_hash == 'ae702fed0238afb5346db8356fe25f13':
|
|
model_params_auto=str('lib_v5/modelparams/1band_sr44100_hl1024.json')
|
|
param_name_auto=str('1band_sr44100_hl1024')
|
|
|
|
#User Models
|
|
|
|
#1 Band
|
|
if '1band_sr16000_hl512' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/1band_sr16000_hl512.json')
|
|
param_name_auto=str('1band_sr16000_hl512')
|
|
if '1band_sr32000_hl512' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/1band_sr32000_hl512.json')
|
|
param_name_auto=str('1band_sr32000_hl512')
|
|
if '1band_sr33075_hl384' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/1band_sr33075_hl384.json')
|
|
param_name_auto=str('1band_sr33075_hl384')
|
|
if '1band_sr44100_hl256' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/1band_sr44100_hl256.json')
|
|
param_name_auto=str('1band_sr44100_hl256')
|
|
if '1band_sr44100_hl512' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/1band_sr44100_hl512.json')
|
|
param_name_auto=str('1band_sr44100_hl512')
|
|
if '1band_sr44100_hl1024' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/1band_sr44100_hl1024.json')
|
|
param_name_auto=str('1band_sr44100_hl1024')
|
|
|
|
#2 Band
|
|
if '2band_44100_lofi' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/2band_44100_lofi.json')
|
|
param_name_auto=str('2band_44100_lofi')
|
|
if '2band_32000' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/2band_32000.json')
|
|
param_name_auto=str('2band_32000')
|
|
if '2band_48000' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/2band_48000.json')
|
|
param_name_auto=str('2band_48000')
|
|
|
|
#3 Band
|
|
if '3band_44100' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/3band_44100.json')
|
|
param_name_auto=str('3band_44100')
|
|
if '3band_44100_mid' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/3band_44100_mid.json')
|
|
param_name_auto=str('3band_44100_mid')
|
|
if '3band_44100_msb2' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/3band_44100_msb2.json')
|
|
param_name_auto=str('3band_44100_msb2')
|
|
|
|
#4 Band
|
|
if '4band_44100' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/4band_44100.json')
|
|
param_name_auto=str('4band_44100')
|
|
if '4band_44100_mid' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/4band_44100_mid.json')
|
|
param_name_auto=str('4band_44100_mid')
|
|
if '4band_44100_msb' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/4band_44100_msb.json')
|
|
param_name_auto=str('4band_44100_msb')
|
|
if '4band_44100_msb2' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/4band_44100_msb2.json')
|
|
param_name_auto=str('4band_44100_msb2')
|
|
if '4band_44100_reverse' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/4band_44100_reverse.json')
|
|
param_name_auto=str('4band_44100_reverse')
|
|
if '4band_44100_sw' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/4band_44100_sw.json')
|
|
param_name_auto=str('4band_44100_sw')
|
|
if '4band_v2' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/4band_v2.json')
|
|
param_name_auto=str('4band_v2')
|
|
if '4band_v2_sn' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/4band_v2_sn.json')
|
|
param_name_auto=str('4band_v2_sn')
|
|
if 'tmodelparam' in ModelName:
|
|
model_params_auto=str('lib_v5/modelparams/tmodelparam.json')
|
|
param_name_auto=str('User Model Param Set')
|
|
|
|
text_widget.write(' Done!\n')
|
|
|
|
|
|
if data['ModelParams'] == 'Auto':
|
|
param_name = param_name_auto
|
|
model_params_d = model_params_auto
|
|
else:
|
|
param_name = str(data['ModelParams'])
|
|
model_params_d = str('lib_v5/modelparams/' + data['ModelParams'])
|
|
|
|
try:
|
|
print('Model Parameters:', model_params_d)
|
|
text_widget.write(base_text + 'Loading assigned model parameters ' + '\"' + param_name + '\"... ')
|
|
except Exception as e:
|
|
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
|
|
errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n'
|
|
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'Model parameters are missing.\n\n')
|
|
text_widget.write(f'Please check the following:\n')
|
|
text_widget.write(f'1. Make sure the model is still present.\n')
|
|
text_widget.write(f'2. If you are running a model that was not originally included in this package, \nplease append the modelparam name to the model name.\n')
|
|
text_widget.write(f' - Example if using \"4band_v2.json\" modelparam: \"model_4band_v2.pth\"\n\n')
|
|
text_widget.write(f'Please address this and try again.\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: VR Architecture\n\n' +
|
|
f'Model parameters are missing.\n\n' +
|
|
f'Please check the following:\n' +
|
|
f'1. Make sure the model is still present.\n' +
|
|
f'2. If you are running a model that was not originally included in this package, please append the modelparam name to the model name.\n' +
|
|
f' - Example if using \"4band_v2.json\" modelparam: \"model_4band_v2.pth\"\n\n' +
|
|
f'Please address this and try again.\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
|
|
torch.cuda.empty_cache()
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
|
|
mp = ModelParameters(model_params_d)
|
|
text_widget.write('Done!\n')
|
|
# -Instrumental-
|
|
if os.path.isfile(data['instrumentalModel']):
|
|
device = torch.device('cpu')
|
|
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
|
|
model.load_state_dict(torch.load(data['instrumentalModel'],
|
|
map_location=device))
|
|
if torch.cuda.is_available() and data['gpu'] >= 0:
|
|
device = torch.device('cuda:{}'.format(data['gpu']))
|
|
model.to(device)
|
|
|
|
vocal_remover.models['instrumental'] = model
|
|
vocal_remover.devices['instrumental'] = device
|
|
|
|
|
|
model_name = os.path.basename(data[f'{data["useModel"]}Model'])
|
|
|
|
mp = ModelParameters(model_params_d)
|
|
|
|
# -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=0.9)
|
|
# 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: VR Architecture\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
|
|
|
|
update_progress(**progress_kwargs,
|
|
step=0.95)
|
|
|
|
# Inverse stft
|
|
y_spec_m = pred * X_phase
|
|
v_spec_m = X_spec_m - y_spec_m
|
|
|
|
def demix_demucs(mix):
|
|
#print('shift_set ', shift_set)
|
|
text_widget.write(base_text + "Running Demucs Inference...\n")
|
|
text_widget.write(base_text + "Processing... ")
|
|
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(demucs, mix[None], split=split_mode, device=device, overlap=overlap_set, shifts=shift_set, progress=False)[0]
|
|
|
|
text_widget.write('Done!\n')
|
|
|
|
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
|
|
sources[[0,1]] = sources[[1,0]]
|
|
|
|
return sources
|
|
|
|
def demucs_prediction(m):
|
|
global demucs_sources
|
|
mix, samplerate = librosa.load(m, mono=False, sr=44100)
|
|
if mix.ndim == 1:
|
|
mix = np.asfortranarray([mix,mix])
|
|
|
|
mix = mix.T
|
|
|
|
demucs_sources = demix_demucs(mix.T)
|
|
|
|
if data['demucsmodelVR']:
|
|
demucs = HDemucs(sources=["other", "vocals"])
|
|
text_widget.write(base_text + 'Loading Demucs model... ')
|
|
update_progress(**progress_kwargs,
|
|
step=0.95)
|
|
path_d = Path('models/Demucs_Models')
|
|
print('What Demucs model was chosen? ', demucs_model_set)
|
|
demucs = _gm(name=demucs_model_set, repo=path_d)
|
|
text_widget.write('Done!\n')
|
|
|
|
print('segment: ', data['segment'])
|
|
|
|
if data['segment'] == 'None':
|
|
segment = None
|
|
if isinstance(demucs, BagOfModels):
|
|
if segment is not None:
|
|
for sub in demucs.models:
|
|
sub.segment = segment
|
|
else:
|
|
if segment is not None:
|
|
sub.segment = segment
|
|
else:
|
|
try:
|
|
segment = int(data['segment'])
|
|
if isinstance(demucs, BagOfModels):
|
|
if segment is not None:
|
|
for sub in demucs.models:
|
|
sub.segment = segment
|
|
else:
|
|
if segment is not None:
|
|
sub.segment = segment
|
|
text_widget.write(base_text + "Segments set to "f"{segment}.\n")
|
|
except:
|
|
segment = None
|
|
if isinstance(demucs, BagOfModels):
|
|
if segment is not None:
|
|
for sub in demucs.models:
|
|
sub.segment = segment
|
|
else:
|
|
if segment is not None:
|
|
sub.segment = segment
|
|
|
|
print('segment port-process: ', segment)
|
|
|
|
demucs.cpu()
|
|
demucs.eval()
|
|
|
|
demucs_prediction(m)
|
|
|
|
if data['voc_only'] and not data['inst_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)
|
|
if data['demucsmodelVR']:
|
|
wav_instrument = spec_utils.cmb_spectrogram_to_wave_d(y_spec_m, mp, input_high_end_h, input_high_end_, demucs=True)
|
|
demucs_inst = demucs_sources[0]
|
|
sources = [wav_instrument,demucs_inst]
|
|
spec = [stft(sources[0],2048,1024),stft(sources[1],2048,1024)]
|
|
ln = min([spec[0].shape[2], spec[1].shape[2]])
|
|
spec[0] = spec[0][:,:,:ln]
|
|
spec[1] = spec[1][:,:,:ln]
|
|
v_spec_c = np.where(np.abs(spec[1]) <= np.abs(spec[0]), spec[1], spec[0])
|
|
wav_instrument = istft(v_spec_c,1024)
|
|
else:
|
|
wav_instrument = spec_utils.cmb_spectrogram_to_wave_d(y_spec_m, mp, input_high_end_h, input_high_end_, demucs=False)
|
|
|
|
if data['voc_only'] and not data['inst_only']:
|
|
pass
|
|
else:
|
|
text_widget.write('Done!\n')
|
|
else:
|
|
wav_instrument = spec_utils.cmb_spectrogram_to_wave_d(y_spec_m, mp)
|
|
if data['voc_only'] and not data['inst_only']:
|
|
pass
|
|
else:
|
|
text_widget.write('Done!\n')
|
|
|
|
if data['inst_only'] and not data['voc_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)
|
|
if data['demucsmodelVR']:
|
|
wav_vocals = spec_utils.cmb_spectrogram_to_wave_d(v_spec_m, mp, input_high_end_h, input_high_end_, demucs=True)
|
|
demucs_voc = demucs_sources[1]
|
|
sources = [wav_vocals,demucs_voc]
|
|
spec = [stft(sources[0],2048,1024),stft(sources[1],2048,1024)]
|
|
ln = min([spec[0].shape[2], spec[1].shape[2]])
|
|
spec[0] = spec[0][:,:,:ln]
|
|
spec[1] = spec[1][:,:,:ln]
|
|
v_spec_c = np.where(np.abs(spec[1]) >= np.abs(spec[0]), spec[1], spec[0])
|
|
wav_vocals = istft(v_spec_c,1024)
|
|
else:
|
|
wav_vocals = spec_utils.cmb_spectrogram_to_wave_d(v_spec_m, mp, input_high_end_h, input_high_end_, demucs=False)
|
|
|
|
if data['inst_only'] and not data['voc_only']:
|
|
pass
|
|
else:
|
|
text_widget.write('Done!\n')
|
|
else:
|
|
wav_vocals = spec_utils.cmb_spectrogram_to_wave_d(v_spec_m, mp, demucs=False)
|
|
if data['inst_only'] and not data['voc_only']:
|
|
pass
|
|
else:
|
|
text_widget.write('Done!\n')
|
|
|
|
update_progress(**progress_kwargs,
|
|
step=1)
|
|
|
|
# Save output music files
|
|
save_files(wav_instrument, wav_vocals)
|
|
|
|
update_progress(**progress_kwargs,
|
|
step=1)
|
|
|
|
# Save output image
|
|
if data['output_image']:
|
|
with open('{}_Instruments.jpg'.format(base_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), 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')
|
|
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: VR Architecture\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: VR Architecture\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: VR Architecture\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: VR Architecture\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: VR Architecture\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 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: VR Architecture\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: VR Architecture\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
|
|
|
|
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: VR Architecture\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
|
|
|
|
try:
|
|
os.remove('temp.wav')
|
|
except:
|
|
pass
|
|
|
|
progress_var.set(0)
|
|
text_widget.write(f'Conversion(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 |