mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-24 07:20:10 +01:00
1226 lines
61 KiB
Python
1226 lines
61 KiB
Python
import os
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from pathlib import Path
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import os.path
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from datetime import datetime
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import pydub
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import shutil
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from random import randrange
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#MDX-Net
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#----------------------------------------
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import soundfile as sf
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import torch
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import numpy as np
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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 demucs.audio import AudioFile
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import time
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import os
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from tqdm import tqdm
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import warnings
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import sys
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import librosa
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import psutil
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#----------------------------------------
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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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# Command line text parsing and widget manipulation
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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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class Predictor():
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def __init__(self):
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pass
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def prediction_setup(self):
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global device
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if data['gpu'] >= 0:
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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if data['gpu'] == -1:
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device = torch.device('cpu')
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if data['demucsmodel']:
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self.demucs = HDemucs(sources=["drums", "bass", "other", "vocals"])
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widget_text.write(base_text + 'Loading Demucs model... ')
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update_progress(**progress_kwargs,
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step=0.05)
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path_d = Path('models/Demucs_Models')
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print('What Demucs model was chosen? ', data['DemucsModel'])
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self.demucs = _gm(name=data['DemucsModel'], repo=path_d)
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widget_text.write('Done!\n')
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if 'UVR' in data['DemucsModel']:
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widget_text.write(base_text + "2 stem model selected.\n")
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if isinstance(self.demucs, BagOfModels):
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widget_text.write(base_text + f"Selected model is a bag of {len(self.demucs.models)} models.\n")
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self.demucs.to(device)
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self.demucs.eval()
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update_progress(**progress_kwargs,
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step=0.1)
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def prediction(self, m):
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mix, samplerate = librosa.load(m, mono=False, sr=44100)
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if mix.ndim == 1:
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mix = np.asfortranarray([mix,mix])
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samplerate = self.demucs.samplerate
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mix = mix.T
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sources = self.demix(mix.T)
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widget_text.write(base_text + 'Inferences complete!\n')
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#Main Save Path
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save_path = os.path.dirname(_basename)
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vocals_name = '(Vocals)'
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other_name = '(Other)'
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drums_name = '(Drums)'
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bass_name = '(Bass)'
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vocals_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(_basename)}_{vocals_name}',)
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vocals_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(_basename)}_{vocals_name}',)
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vocals_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(_basename)}_{vocals_name}',)
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#Other
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other_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(_basename)}_{other_name}',)
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other_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(_basename)}_{other_name}',)
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other_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(_basename)}_{other_name}',)
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#Drums
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drums_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(_basename)}_{drums_name}',)
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drums_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(_basename)}_{drums_name}',)
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drums_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(_basename)}_{drums_name}',)
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#Bass
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bass_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(_basename)}_{bass_name}',)
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bass_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(_basename)}_{bass_name}',)
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bass_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(_basename)}_{bass_name}',)
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#If not 'All Stems'
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if stemset_n == '(Vocals)':
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vocal_name = '(Vocals)'
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elif stemset_n == '(Other)':
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vocal_name = '(Other)'
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elif stemset_n == '(Drums)':
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vocal_name = '(Drums)'
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elif stemset_n == '(Bass)':
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vocal_name = '(Bass)'
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elif stemset_n == '(Instrumental)':
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vocal_name = '(Instrumental)'
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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(_basename)}_{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(_basename)}_{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(_basename)}_{vocal_name}',)
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#Instrumental Path
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if stemset_n == '(Vocals)':
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Instrumental_name = '(Instrumental)'
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elif stemset_n == '(Other)':
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Instrumental_name = '(No_Other)'
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elif stemset_n == '(Drums)':
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Instrumental_name = '(No_Drums)'
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elif stemset_n == '(Bass)':
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Instrumental_name = '(No_Bass)'
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elif stemset_n == '(Instrumental)':
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if data['demucs_stems'] == 'All Stems':
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Instrumental_name = '(Instrumental)'
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else:
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Instrumental_name = '(Vocals)'
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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(_basename)}_{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(_basename)}_{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(_basename)}_{Instrumental_name}',)
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if os.path.isfile(vocal_path):
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file_exists_n = 'there'
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else:
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file_exists_n = 'not_there'
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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 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 not data['demucs_stems'] == 'All Stems':
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if data['inst_only_b']:
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widget_text.write(base_text + 'Preparing mixture without selected stem...')
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else:
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widget_text.write(base_text + 'Saving Stem(s)... ')
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else:
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pass
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if data['demucs_stems'] == 'All Stems':
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if data['saveFormat'] == 'Wav':
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widget_text.write(base_text + 'Saving Stem(s)... ')
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else:
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pass
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if 'UVR' in model_set_name:
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sf.write(Instrumental_path, sources[0].T, samplerate)
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sf.write(vocals_path, sources[1].T, samplerate)
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else:
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sf.write(bass_path, sources[0].T, samplerate)
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sf.write(drums_path, sources[1].T, samplerate)
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sf.write(other_path, sources[2].T, samplerate)
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sf.write(vocals_path, sources[3].T, samplerate)
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if data['saveFormat'] == 'Mp3':
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try:
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if 'UVR' in model_set_name:
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widget_text.write(base_text + 'Saving Stem(s) as Mp3(s)... ')
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musfile = pydub.AudioSegment.from_wav(vocals_path)
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musfile.export(vocals_path_mp3, format="mp3", bitrate="320k")
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musfile = pydub.AudioSegment.from_wav(Instrumental_path)
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musfile.export(Instrumental_path_mp3, format="mp3", bitrate="320k")
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try:
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os.remove(Instrumental_path)
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os.remove(vocals_path)
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except:
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pass
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else:
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widget_text.write(base_text + 'Saving Stem(s) as Mp3(s)... ')
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musfile = pydub.AudioSegment.from_wav(drums_path)
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musfile.export(drums_path_mp3, format="mp3", bitrate="320k")
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musfile = pydub.AudioSegment.from_wav(bass_path)
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musfile.export(bass_path_mp3, format="mp3", bitrate="320k")
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musfile = pydub.AudioSegment.from_wav(other_path)
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musfile.export(other_path_mp3, format="mp3", bitrate="320k")
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musfile = pydub.AudioSegment.from_wav(vocals_path)
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musfile.export(vocals_path_mp3, format="mp3", bitrate="320k")
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try:
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os.remove(drums_path)
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os.remove(bass_path)
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os.remove(other_path)
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os.remove(vocals_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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widget_text.write('\n' + base_text + 'Failed to save output(s) as Mp3(s).\n')
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widget_text.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n')
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widget_text.write(base_text + 'Moving on...\n')
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else:
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widget_text.write(base_text + 'Failed to save output(s) as Mp3(s).\n')
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widget_text.write(base_text + 'Please check error log.\n')
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widget_text.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\n' +
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f'Process Method: Demucs v3\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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elif data['saveFormat'] == 'Flac':
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try:
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if 'UVR' in model_set_name:
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widget_text.write(base_text + 'Saving Stem(s) as flac(s)... ')
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musfile = pydub.AudioSegment.from_wav(vocals_path)
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musfile.export(vocals_path_flac, format="flac")
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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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try:
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os.remove(Instrumental_path)
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os.remove(vocals_path)
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except:
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pass
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else:
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widget_text.write(base_text + 'Saving Stem(s) as Flac(s)... ')
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musfile = pydub.AudioSegment.from_wav(drums_path)
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musfile.export(drums_path_flac, format="flac")
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musfile = pydub.AudioSegment.from_wav(bass_path)
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musfile.export(bass_path_flac, format="flac")
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musfile = pydub.AudioSegment.from_wav(other_path)
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musfile.export(other_path_flac, format="flac")
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musfile = pydub.AudioSegment.from_wav(vocals_path)
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musfile.export(vocals_path_flac, format="flac")
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try:
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os.remove(drums_path)
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os.remove(bass_path)
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os.remove(other_path)
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os.remove(vocals_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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widget_text.write('\n' + base_text + 'Failed to save output(s) as Flac(s).\n')
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widget_text.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n')
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widget_text.write(base_text + 'Moving on...\n')
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else:
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widget_text.write(base_text + 'Failed to save output(s) as flac(s).\n')
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widget_text.write(base_text + 'Please check error log.\n')
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widget_text.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\n' +
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f'Process Method: Demucs v3\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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elif data['saveFormat'] == 'Wav':
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pass
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widget_text.write('Done!\n')
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else:
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if 'UVR' in model_set_name:
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if stemset_n == '(Vocals)':
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sf.write(vocal_path, sources[1].T, samplerate)
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else:
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sf.write(vocal_path, sources[source_val].T, samplerate)
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else:
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sf.write(vocal_path, sources[source_val].T, samplerate)
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widget_text.write('Done!\n')
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update_progress(**progress_kwargs,
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step=(0.9))
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if data['demucs_stems'] == 'All Stems':
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pass
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else:
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if data['voc_only_b'] and not data['inst_only_b']:
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pass
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else:
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finalfiles = [
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{
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'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json',
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'files':[str(music_file), vocal_path],
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}
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]
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widget_text.write(base_text + 'Saving Instrumental... ')
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for i, e in tqdm(enumerate(finalfiles)):
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wave, specs = {}, {}
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mp = ModelParameters(e['model_params'])
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for i in range(len(e['files'])):
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spec = {}
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for d in range(len(mp.param['band']), 0, -1):
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bp = mp.param['band'][d]
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if d == len(mp.param['band']): # high-end band
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wave[d], _ = librosa.load(
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e['files'][i], bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
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if len(wave[d].shape) == 1: # mono to stereo
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wave[d] = np.array([wave[d], wave[d]])
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else: # lower bands
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wave[d] = librosa.resample(wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
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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'])
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specs[i] = spec_utils.combine_spectrograms(spec, mp)
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del wave
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ln = min([specs[0].shape[2], specs[1].shape[2]])
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specs[0] = specs[0][:,:,:ln]
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specs[1] = specs[1][:,:,:ln]
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X_mag = np.abs(specs[0])
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y_mag = np.abs(specs[1])
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max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
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v_spec = specs[1] - max_mag * np.exp(1.j * np.angle(specs[0]))
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update_progress(**progress_kwargs,
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step=(1))
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sf.write(Instrumental_path, spec_utils.cmb_spectrogram_to_wave(-v_spec, mp), mp.param['sr'])
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if data['inst_only_b']:
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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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widget_text.write('Done!\n')
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if not data['demucs_stems'] == 'All Stems':
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if data['saveFormat'] == 'Mp3':
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try:
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if data['inst_only_b'] == 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="320k")
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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_b'] == 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="320k")
|
|
if file_exists_i == 'there':
|
|
pass
|
|
else:
|
|
try:
|
|
os.remove(Instrumental_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: Demucs v3\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_b'] == True:
|
|
pass
|
|
else:
|
|
musfile = pydub.AudioSegment.from_wav(vocal_path)
|
|
musfile.export(vocal_path_flac, format="flac")
|
|
if file_exists_v == 'there':
|
|
pass
|
|
else:
|
|
try:
|
|
os.remove(vocal_path)
|
|
except:
|
|
pass
|
|
if data['voc_only_b'] == True:
|
|
pass
|
|
else:
|
|
musfile = pydub.AudioSegment.from_wav(Instrumental_path)
|
|
musfile.export(Instrumental_path_flac, format="flac")
|
|
if file_exists_i == 'there':
|
|
pass
|
|
else:
|
|
try:
|
|
os.remove(Instrumental_path)
|
|
except:
|
|
pass
|
|
|
|
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: Demucs v3\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['inst_only_b']:
|
|
if file_exists_n == 'there':
|
|
pass
|
|
else:
|
|
try:
|
|
os.remove(vocal_path)
|
|
except:
|
|
pass
|
|
else:
|
|
try:
|
|
os.remove(vocal_path)
|
|
except:
|
|
pass
|
|
|
|
widget_text.write(base_text + 'Completed Seperation!\n')
|
|
|
|
def demix(self, mix):
|
|
|
|
# 1 = demucs only
|
|
# 0 = onnx only
|
|
if data['chunks'] == 'Full':
|
|
chunk_set = 0
|
|
else:
|
|
chunk_set = data['chunks']
|
|
|
|
if data['chunks'] == 'Auto':
|
|
if split_mode == True:
|
|
widget_text.write(base_text + "Split Mode is on (Chunks disabled).\n")
|
|
chunk_set = 0
|
|
else:
|
|
widget_text.write(base_text + "Split Mode is off (Chunks enabled).\n")
|
|
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(10)
|
|
widget_text.write(base_text + 'Chunk size auto-set to 10... \n')
|
|
if gpu_mem in [7, 8, 9]:
|
|
chunk_set = int(30)
|
|
widget_text.write(base_text + 'Chunk size auto-set to 30... \n')
|
|
if gpu_mem in [10, 11, 12, 13, 14, 15]:
|
|
chunk_set = int(50)
|
|
widget_text.write(base_text + 'Chunk size auto-set to 50... \n')
|
|
if int(gpu_mem) >= int(16):
|
|
chunk_set = int(0)
|
|
widget_text.write(base_text + 'Chunk size auto-set to Full... \n')
|
|
if data['gpu'] == -1:
|
|
sys_mem = psutil.virtual_memory().total >> 30
|
|
if int(sys_mem) <= int(4):
|
|
chunk_set = int(5)
|
|
widget_text.write(base_text + 'Chunk size auto-set to 5... \n')
|
|
if sys_mem in [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]:
|
|
chunk_set = int(10)
|
|
widget_text.write(base_text + 'Chunk size auto-set to 10... \n')
|
|
if sys_mem in [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32]:
|
|
chunk_set = int(40)
|
|
widget_text.write(base_text + 'Chunk size auto-set to 40... \n')
|
|
if int(sys_mem) >= int(33):
|
|
chunk_set = int(0)
|
|
widget_text.write(base_text + 'Chunk size auto-set to Full... \n')
|
|
elif data['chunks'] == 'Full':
|
|
chunk_set = 0
|
|
widget_text.write(base_text + "Chunk size set to full... \n")
|
|
else:
|
|
if split_mode == True:
|
|
widget_text.write(base_text + "Split Mode is on (Chunks disabled).\n")
|
|
chunk_set = 0
|
|
else:
|
|
widget_text.write(base_text + "Split Mode is off (Chunks enabled).\n")
|
|
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
|
|
|
|
|
|
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
|
|
|
|
sources = self.demix_demucs(segmented_mix, margin_size=margin)
|
|
|
|
return sources
|
|
|
|
def demix_demucs(self, mix, margin_size):
|
|
|
|
processed = {}
|
|
demucsitera = len(mix)
|
|
demucsitera_calc = demucsitera * 2
|
|
gui_progress_bar_demucs = 0
|
|
|
|
widget_text.write(base_text + "Running Demucs Inference...\n")
|
|
widget_text.write(base_text + "Processing "f"{len(mix)} slices... ")
|
|
print(' Running Demucs Inference...')
|
|
for nmix in mix:
|
|
gui_progress_bar_demucs += 1
|
|
update_progress(**progress_kwargs,
|
|
step=(0.1 + (1.7/demucsitera_calc * gui_progress_bar_demucs)))
|
|
cmix = mix[nmix]
|
|
cmix = torch.tensor(cmix, dtype=torch.float32)
|
|
ref = cmix.mean(0)
|
|
cmix = (cmix - ref.mean()) / ref.std()
|
|
with torch.no_grad():
|
|
sources = apply_model(self.demucs, cmix[None], split=split_mode, device=device, overlap=overlap_set, shifts=shift_set, progress=False)[0]
|
|
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
|
|
sources[[0,1]] = sources[[1,0]]
|
|
|
|
start = 0 if nmix == 0 else margin_size
|
|
end = None if nmix == list(mix.keys())[::-1][0] else -margin_size
|
|
if margin_size == 0:
|
|
end = None
|
|
processed[nmix] = sources[:,:,start:end].copy()
|
|
|
|
sources = list(processed.values())
|
|
sources = np.concatenate(sources, axis=-1)
|
|
widget_text.write('Done!\n')
|
|
return sources
|
|
|
|
data = {
|
|
# Paths
|
|
'input_paths': None,
|
|
'export_path': None,
|
|
'saveFormat': 'Wav',
|
|
# Processing Options
|
|
'demucsmodel': True,
|
|
'gpu': -1,
|
|
'chunks': 'Full',
|
|
'modelFolder': False,
|
|
'voc_only_b': False,
|
|
'inst_only_b': False,
|
|
'overlap_b': 0.25,
|
|
'shifts_b': 2,
|
|
'margin': 44100,
|
|
'split_mode': False,
|
|
'compensate': 1.03597672895,
|
|
'demucs_stems': 'All Stems',
|
|
'DemucsModel': 'mdx_extra',
|
|
'audfile': True,
|
|
}
|
|
default_chunks = data['chunks']
|
|
|
|
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 _basename
|
|
global _mixture
|
|
global progress_kwargs
|
|
global base_text
|
|
global model_set_name
|
|
global stemset_n
|
|
global channel_set
|
|
global margin_set
|
|
global overlap_set
|
|
global shift_set
|
|
global source_val
|
|
global split_mode
|
|
|
|
# Update default settings
|
|
default_chunks = data['chunks']
|
|
|
|
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
|
|
|
|
timestampnum = round(datetime.utcnow().timestamp())
|
|
randomnum = randrange(100000, 1000000)
|
|
|
|
data.update(kwargs)
|
|
|
|
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)):
|
|
|
|
model_set_name = data['DemucsModel']
|
|
|
|
if data['demucs_stems'] == 'Vocals':
|
|
source_val = 3
|
|
stemset_n = '(Vocals)'
|
|
if data['demucs_stems'] == 'Other':
|
|
if 'UVR' in model_set_name:
|
|
source_val = 0
|
|
stemset_n = '(Instrumental)'
|
|
else:
|
|
source_val = 2
|
|
stemset_n = '(Other)'
|
|
if data['demucs_stems'] == 'Drums':
|
|
if 'UVR' in model_set_name:
|
|
text_widget.write('You can only choose "Vocals" or "Other" stems when using this model.\n')
|
|
text_widget.write('Please select one of the stock Demucs models 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:
|
|
source_val = 1
|
|
stemset_n = '(Drums)'
|
|
if data['demucs_stems'] == 'Bass':
|
|
if 'UVR' in model_set_name:
|
|
text_widget.write('You can only choose "Vocals" or "Other" stems when using this model.\n')
|
|
text_widget.write('Please select one of the stock Demucs models 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:
|
|
source_val = 0
|
|
stemset_n = '(Bass)'
|
|
if data['demucs_stems'] == 'All Stems':
|
|
source_val = 3
|
|
stemset_n = '(Instrumental)'
|
|
|
|
|
|
overlap_set = float(data['overlap_b'])
|
|
channel_set = int(data['channel'])
|
|
margin_set = int(data['margin'])
|
|
shift_set = int(data['shifts_b'])
|
|
split_mode = data['split_mode']
|
|
|
|
def determinemodelFolderName():
|
|
"""
|
|
Determine the name that is used for the folder and appended
|
|
to the back of the music files
|
|
"""
|
|
modelFolderName = ''
|
|
|
|
if str(model_set_name):
|
|
modelFolderName += os.path.splitext(os.path.basename(model_set_name))[0]
|
|
|
|
if modelFolderName:
|
|
|
|
modelFolderName = '/' + modelFolderName
|
|
|
|
|
|
return modelFolderName
|
|
|
|
|
|
if data['audfile'] == True:
|
|
modelFolderName = determinemodelFolderName()
|
|
|
|
if modelFolderName:
|
|
folder_path = f'{data["export_path"]}{modelFolderName}'
|
|
if not os.path.isdir(folder_path):
|
|
os.mkdir(folder_path)
|
|
|
|
_mixture = f'{data["input_paths"]}'
|
|
if data['modelFolder']:
|
|
try:
|
|
_basename = f'{data["export_path"]}{modelFolderName}/{file_num}_{str(timestampnum)}_{os.path.splitext(os.path.basename(music_file))[0]}'
|
|
except:
|
|
_basename = f'{data["export_path"]}{modelFolderName}/{file_num}_{str(randomnum)}_{os.path.splitext(os.path.basename(music_file))[0]}'
|
|
else:
|
|
_basename = f'{data["export_path"]}{modelFolderName}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
|
|
else:
|
|
_mixture = f'{data["input_paths"]}'
|
|
if data['modelFolder']:
|
|
try:
|
|
_basename = f'{data["export_path"]}/{file_num}_{str(timestampnum)}_{model_set_name}_{os.path.splitext(os.path.basename(music_file))[0]}'
|
|
except:
|
|
_basename = f'{data["export_path"]}/{file_num}_{str(randomnum)}_{model_set_name}_{os.path.splitext(os.path.basename(music_file))[0]}'
|
|
else:
|
|
_basename = f'{data["export_path"]}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
|
|
|
|
#if ('models/MDX_Net_Models/' + model_set + '.onnx')
|
|
|
|
|
|
# -Get text and update progress-
|
|
base_text = get_baseText(total_files=len(data['input_paths']),
|
|
file_num=file_num)
|
|
progress_kwargs = {'progress_var': progress_var,
|
|
'total_files': len(data['input_paths']),
|
|
'file_num': file_num}
|
|
|
|
try:
|
|
|
|
total, used, free = shutil.disk_usage("/")
|
|
|
|
|
|
total_space = int(total/1.074e+9)
|
|
used_space = int(used/1.074e+9)
|
|
free_space = int(free/1.074e+9)
|
|
|
|
|
|
if int(free/1.074e+9) <= int(2):
|
|
text_widget.write('Error: Not enough storage on main drive to continue. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly. \n\nPlease ensure your main drive has at least 3 GB\'s of storage and try again.\n\n')
|
|
text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n')
|
|
text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n')
|
|
text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n')
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
if int(free/1.074e+9) in [3, 4, 5, 6, 7, 8]:
|
|
text_widget.write('Warning: Your main drive is running low on storage. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly.\n\n')
|
|
text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n')
|
|
text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n')
|
|
text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n\n')
|
|
except:
|
|
pass
|
|
|
|
update_progress(**progress_kwargs,
|
|
step=0)
|
|
|
|
e = os.path.join(data["export_path"])
|
|
|
|
demucsmodel = 'models/Demucs_Models/' + str(data['DemucsModel'])
|
|
|
|
pred = Predictor()
|
|
pred.prediction_setup()
|
|
|
|
# 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: Demucs v3\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: Demucs v3\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: Demucs v3\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: Demucs v3\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: Demucs v3\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: Demucs v3\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: Demucs v3\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: Demucs v3\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: Demucs v3\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: Demucs v3\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: Demucs v3\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: Demucs v3\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))
|
|
|