mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-09-24 11:38:37 +02:00
1409 lines
67 KiB
Python
1409 lines
67 KiB
Python
import os
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import subprocess
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from unittest import skip
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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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import hashlib
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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 pathlib
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from models import get_models, spec_effects
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import onnxruntime as ort
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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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from typing import Literal
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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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print('Print the gpu setting: ', data['gpu'])
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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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if 'UVR' in demucs_model_set:
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self.demucs = HDemucs(sources=["other", "vocals"])
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else:
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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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self.demucs = _gm(name=demucs_model_set, repo=path_d)
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self.demucs.to(device)
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self.demucs.eval()
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widget_text.write('Done!\n')
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if isinstance(self.demucs, BagOfModels):
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widget_text.write(base_text + f"Selected Demucs model is a bag of {len(self.demucs.models)} model(s).\n")
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self.onnx_models = {}
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c = 0
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self.models = get_models('tdf_extra', load=False, device=cpu, stems=modeltype, n_fft_scale=n_fft_scale_set, dim_f=dim_f_set)
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if not data['demucs_only']:
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widget_text.write(base_text + 'Loading ONNX model... ')
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update_progress(**progress_kwargs,
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step=0.1)
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c+=1
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if data['gpu'] >= 0:
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if torch.cuda.is_available():
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run_type = ['CUDAExecutionProvider']
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else:
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data['gpu'] = -1
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widget_text.write("\n" + base_text + "No NVIDIA GPU detected. Switching to CPU... ")
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run_type = ['CPUExecutionProvider']
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elif data['gpu'] == -1:
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run_type = ['CPUExecutionProvider']
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print('Selected Model: ', model_set)
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self.onnx_models[c] = ort.InferenceSession(os.path.join('models/MDX_Net_Models', str(model_set) + '.onnx'), providers=run_type)
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if not data['demucs_only']:
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widget_text.write('Done!\n')
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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 = 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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c = -1
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#Main Save Path
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save_path = os.path.dirname(_basename)
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print('stemset_n: ', stemset_n)
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#Vocal Path
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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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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(_basename)}_{vocal_name}_{model_set_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}_{model_set_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}_{model_set_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(_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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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(_basename)}_{Instrumental_name}_{model_set_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}_{model_set_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}_{model_set_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(_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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#Non-Reduced Vocal Path
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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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if data['modelFolder']:
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non_reduced_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}_{model_set_name}_No_Reduction',)
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non_reduced_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}_{model_set_name}_No_Reduction',)
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non_reduced_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}_{model_set_name}_No_Reduction',)
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else:
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non_reduced_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}_No_Reduction',)
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non_reduced_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}_No_Reduction',)
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non_reduced_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}_No_Reduction',)
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if os.path.isfile(non_reduced_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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print('Is there already a voc file there? ', file_exists_v)
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if not data['noisereduc_s'] == 'None':
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c += 1
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if not data['demucsmodel']:
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if data['inst_only']:
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widget_text.write(base_text + 'Preparing to save Instrumental...')
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else:
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widget_text.write(base_text + 'Saving vocals... ')
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sf.write(non_reduced_vocal_path, sources[c].T, samplerate)
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update_progress(**progress_kwargs,
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step=(0.9))
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widget_text.write('Done!\n')
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widget_text.write(base_text + 'Performing Noise Reduction... ')
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reduction_sen = float(int(data['noisereduc_s'])/10)
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subprocess.call("lib_v5\\sox\\sox.exe" + ' "' +
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f"{str(non_reduced_vocal_path)}" + '" "' + f"{str(vocal_path)}" + '" ' +
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"noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}",
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shell=True, stdout=subprocess.PIPE,
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stdin=subprocess.PIPE, stderr=subprocess.PIPE)
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widget_text.write('Done!\n')
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update_progress(**progress_kwargs,
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step=(0.95))
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else:
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if data['inst_only']:
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widget_text.write(base_text + 'Preparing Instrumental...')
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else:
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widget_text.write(base_text + 'Saving Vocals... ')
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if data['demucs_only']:
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if 'UVR' in demucs_model_set:
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sf.write(non_reduced_vocal_path, sources[1].T, samplerate)
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else:
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sf.write(non_reduced_vocal_path, sources[source_val].T, samplerate)
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update_progress(**progress_kwargs,
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step=(0.9))
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widget_text.write('Done!\n')
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widget_text.write(base_text + 'Performing Noise Reduction... ')
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reduction_sen = float(data['noisereduc_s'])/10
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print(noise_pro_set)
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subprocess.call("lib_v5\\sox\\sox.exe" + ' "' +
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f"{str(non_reduced_vocal_path)}" + '" "' + f"{str(vocal_path)}" + '" ' +
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"noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}",
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shell=True, stdout=subprocess.PIPE,
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stdin=subprocess.PIPE, stderr=subprocess.PIPE)
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update_progress(**progress_kwargs,
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step=(0.95))
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widget_text.write('Done!\n')
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else:
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c += 1
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if not data['demucsmodel']:
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if data['inst_only']:
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widget_text.write(base_text + 'Preparing Instrumental...')
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else:
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widget_text.write(base_text + 'Saving Vocals... ')
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sf.write(vocal_path, sources[c].T, samplerate)
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update_progress(**progress_kwargs,
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step=(0.9))
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widget_text.write('Done!\n')
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else:
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if data['inst_only']:
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widget_text.write(base_text + 'Preparing Instrumental...')
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else:
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widget_text.write(base_text + 'Saving Vocals... ')
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if data['demucs_only']:
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if 'UVR' in demucs_model_set:
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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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update_progress(**progress_kwargs,
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step=(0.9))
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widget_text.write('Done!\n')
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if data['voc_only'] and not data['inst_only']:
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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']:
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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 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="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'] == 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")
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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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if data['non_red'] == True:
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musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path)
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musfile.export(non_reduced_vocal_path_mp3, format="mp3", bitrate="320k")
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if file_exists_n == 'there':
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pass
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else:
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try:
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os.remove(non_reduced_vocal_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(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: MDX-Net\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 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:
|
|
pass
|
|
else:
|
|
musfile = pydub.AudioSegment.from_wav(Instrumental_path)
|
|
musfile.export(Instrumental_path_flac, format="flac")
|
|
if file_exists_i == 'there':
|
|
pass
|
|
else:
|
|
try:
|
|
os.remove(Instrumental_path)
|
|
except:
|
|
pass
|
|
if data['non_red'] == True:
|
|
musfile = pydub.AudioSegment.from_wav(non_reduced_vocal_path)
|
|
musfile.export(non_reduced_vocal_path_flac, format="flac")
|
|
if file_exists_n == 'there':
|
|
pass
|
|
else:
|
|
try:
|
|
os.remove(non_reduced_vocal_path)
|
|
except:
|
|
pass
|
|
|
|
except Exception as e:
|
|
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
|
|
errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n'
|
|
if "ffmpeg" in errmessage:
|
|
widget_text.write(base_text + 'Failed to save output(s) as Flac(s).\n')
|
|
widget_text.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n')
|
|
widget_text.write(base_text + 'Moving on...\n')
|
|
else:
|
|
widget_text.write(base_text + 'Failed to save output(s) as Flac(s).\n')
|
|
widget_text.write(base_text + 'Please check error log.\n')
|
|
widget_text.write(base_text + 'Moving on...\n')
|
|
try:
|
|
with open('errorlog.txt', 'w') as f:
|
|
f.write(f'Last Error Received:\n\n' +
|
|
f'Error Received while attempting to save file as flac "{os.path.basename(music_file)}":\n\n' +
|
|
f'Process Method: MDX-Net\n\n' +
|
|
f'FFmpeg might be missing or corrupted.\n\n' +
|
|
f'If this error persists, please contact the developers.\n\n' +
|
|
f'Raw error details:\n\n' +
|
|
errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
|
|
except:
|
|
pass
|
|
|
|
if data['noisereduc_s'] == 'None':
|
|
pass
|
|
elif data['non_red'] == True:
|
|
pass
|
|
elif data['inst_only']:
|
|
if file_exists_n == 'there':
|
|
pass
|
|
else:
|
|
try:
|
|
os.remove(non_reduced_vocal_path)
|
|
except:
|
|
pass
|
|
else:
|
|
try:
|
|
os.remove(non_reduced_vocal_path)
|
|
except:
|
|
pass
|
|
|
|
widget_text.write(base_text + 'Completed Seperation!\n')
|
|
|
|
def demix(self, mix):
|
|
# 1 = demucs only
|
|
# 0 = onnx only
|
|
if data['chunks'] == 'Full':
|
|
chunk_set = 0
|
|
else:
|
|
chunk_set = data['chunks']
|
|
|
|
if data['chunks'] == 'Auto':
|
|
if data['gpu'] == 0:
|
|
try:
|
|
gpu_mem = round(torch.cuda.get_device_properties(0).total_memory/1.074e+9)
|
|
except:
|
|
widget_text.write(base_text + 'NVIDIA GPU Required for conversion!\n')
|
|
if int(gpu_mem) <= int(6):
|
|
chunk_set = int(5)
|
|
widget_text.write(base_text + 'Chunk size auto-set to 5... \n')
|
|
if gpu_mem in [7, 8, 9, 10, 11, 12, 13, 14, 15]:
|
|
chunk_set = int(10)
|
|
widget_text.write(base_text + 'Chunk size auto-set to 10... \n')
|
|
if int(gpu_mem) >= int(16):
|
|
chunk_set = int(40)
|
|
widget_text.write(base_text + 'Chunk size auto-set to 40... \n')
|
|
if data['gpu'] == -1:
|
|
sys_mem = psutil.virtual_memory().total >> 30
|
|
if int(sys_mem) <= int(4):
|
|
chunk_set = int(1)
|
|
widget_text.write(base_text + 'Chunk size auto-set to 1... \n')
|
|
if sys_mem in [5, 6, 7, 8]:
|
|
chunk_set = int(10)
|
|
widget_text.write(base_text + 'Chunk size auto-set to 10... \n')
|
|
if sys_mem in [9, 10, 11, 12, 13, 14, 15, 16]:
|
|
chunk_set = int(25)
|
|
widget_text.write(base_text + 'Chunk size auto-set to 25... \n')
|
|
if int(sys_mem) >= int(17):
|
|
chunk_set = int(60)
|
|
widget_text.write(base_text + 'Chunk size auto-set to 60... \n')
|
|
elif data['chunks'] == 'Full':
|
|
chunk_set = 0
|
|
widget_text.write(base_text + "Chunk size set to full... \n")
|
|
else:
|
|
chunk_set = int(data['chunks'])
|
|
widget_text.write(base_text + "Chunk size user-set to "f"{chunk_set}... \n")
|
|
|
|
samples = mix.shape[-1]
|
|
margin = margin_set
|
|
chunk_size = chunk_set*44100
|
|
assert not margin == 0, 'margin cannot be zero!'
|
|
if margin > chunk_size:
|
|
margin = chunk_size
|
|
|
|
b = np.array([[[0.5]], [[0.5]], [[0.7]], [[0.9]]])
|
|
segmented_mix = {}
|
|
|
|
if chunk_set == 0 or samples < chunk_size:
|
|
chunk_size = samples
|
|
|
|
counter = -1
|
|
for skip in range(0, samples, chunk_size):
|
|
counter+=1
|
|
|
|
s_margin = 0 if counter == 0 else margin
|
|
end = min(skip+chunk_size+margin, samples)
|
|
|
|
start = skip-s_margin
|
|
|
|
segmented_mix[skip] = mix[:,start:end].copy()
|
|
if end == samples:
|
|
break
|
|
|
|
|
|
if not data['demucsmodel']:
|
|
sources = self.demix_base(segmented_mix, margin_size=margin)
|
|
elif data['demucs_only']:
|
|
if split_mode == True:
|
|
sources = self.demix_demucs_split(mix)
|
|
if split_mode == False:
|
|
sources = self.demix_demucs(segmented_mix, margin_size=margin)
|
|
else: # both, apply spec effects
|
|
base_out = self.demix_base(segmented_mix, margin_size=margin)
|
|
print(split_mode)
|
|
if split_mode == True:
|
|
demucs_out = self.demix_demucs_split(mix)
|
|
if split_mode == False:
|
|
demucs_out = self.demix_demucs(segmented_mix, margin_size=margin)
|
|
nan_count = np.count_nonzero(np.isnan(demucs_out)) + np.count_nonzero(np.isnan(base_out))
|
|
if nan_count > 0:
|
|
print('Warning: there are {} nan values in the array(s).'.format(nan_count))
|
|
demucs_out, base_out = np.nan_to_num(demucs_out), np.nan_to_num(base_out)
|
|
sources = {}
|
|
print(data['mixing'])
|
|
|
|
if 'UVR' in demucs_model_set:
|
|
sources[source_val] = (spec_effects(wave=[demucs_out[1],base_out[0]],
|
|
algorithm=data['mixing'],
|
|
value=b[source_val])*float(data['compensate'])) # compensation
|
|
else:
|
|
sources[source_val] = (spec_effects(wave=[demucs_out[source_val],base_out[0]],
|
|
algorithm=data['mixing'],
|
|
value=b[source_val])*float(data['compensate'])) # compensation
|
|
return sources
|
|
|
|
def demix_base(self, mixes, margin_size):
|
|
chunked_sources = []
|
|
onnxitera = len(mixes)
|
|
onnxitera_calc = onnxitera * 2
|
|
gui_progress_bar_onnx = 0
|
|
widget_text.write(base_text + "Running ONNX Inference...\n")
|
|
widget_text.write(base_text + "Processing "f"{onnxitera} slices... ")
|
|
print(' Running ONNX Inference...')
|
|
|
|
for mix in mixes:
|
|
gui_progress_bar_onnx += 1
|
|
if data['demucsmodel']:
|
|
update_progress(**progress_kwargs,
|
|
step=(0.1 + (0.5/onnxitera_calc * gui_progress_bar_onnx)))
|
|
else:
|
|
update_progress(**progress_kwargs,
|
|
step=(0.1 + (0.9/onnxitera * gui_progress_bar_onnx)))
|
|
|
|
cmix = mixes[mix]
|
|
sources = []
|
|
n_sample = cmix.shape[1]
|
|
|
|
mod = 0
|
|
for model in self.models:
|
|
mod += 1
|
|
trim = model.n_fft//2
|
|
gen_size = model.chunk_size-2*trim
|
|
pad = gen_size - n_sample%gen_size
|
|
mix_p = np.concatenate((np.zeros((2,trim)), cmix, np.zeros((2,pad)), np.zeros((2,trim))), 1)
|
|
mix_waves = []
|
|
i = 0
|
|
while i < n_sample + pad:
|
|
waves = np.array(mix_p[:, i:i+model.chunk_size])
|
|
mix_waves.append(waves)
|
|
i += gen_size
|
|
mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu)
|
|
with torch.no_grad():
|
|
_ort = self.onnx_models[mod]
|
|
spek = model.stft(mix_waves)
|
|
|
|
tar_waves = model.istft(torch.tensor(_ort.run(None, {'input': spek.cpu().numpy()})[0]))#.cpu()
|
|
|
|
tar_signal = tar_waves[:,:,trim:-trim].transpose(0,1).reshape(2, -1).numpy()[:, :-pad]
|
|
|
|
start = 0 if mix == 0 else margin_size
|
|
end = None if mix == list(mixes.keys())[::-1][0] else -margin_size
|
|
if margin_size == 0:
|
|
end = None
|
|
sources.append(tar_signal[:,start:end])
|
|
|
|
chunked_sources.append(sources)
|
|
_sources = np.concatenate(chunked_sources, axis=-1)
|
|
del self.onnx_models
|
|
widget_text.write('Done!\n')
|
|
return _sources
|
|
|
|
def demix_demucs(self, mix, margin_size):
|
|
print('shift_set ', shift_set)
|
|
processed = {}
|
|
demucsitera = len(mix)
|
|
demucsitera_calc = demucsitera * 2
|
|
gui_progress_bar_demucs = 0
|
|
widget_text.write(base_text + "Split Mode is off. (Chunks enabled for Demucs Model)\n")
|
|
widget_text.write(base_text + "Running Demucs Inference...\n")
|
|
widget_text.write(base_text + "Processing "f"{len(mix)} slices... ")
|
|
print(' Running Demucs Inference...')
|
|
for nmix in mix:
|
|
gui_progress_bar_demucs += 1
|
|
update_progress(**progress_kwargs,
|
|
step=(0.35 + (1.05/demucsitera_calc * gui_progress_bar_demucs)))
|
|
cmix = mix[nmix]
|
|
cmix = torch.tensor(cmix, dtype=torch.float32)
|
|
ref = cmix.mean(0)
|
|
cmix = (cmix - ref.mean()) / ref.std()
|
|
with torch.no_grad():
|
|
print(split_mode)
|
|
sources = apply_model(self.demucs, cmix[None], split=split_mode, device=device, overlap=overlap_set, shifts=shift_set, progress=False)[0]
|
|
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
|
|
sources[[0,1]] = sources[[1,0]]
|
|
|
|
start = 0 if nmix == 0 else margin_size
|
|
end = None if nmix == list(mix.keys())[::-1][0] else -margin_size
|
|
if margin_size == 0:
|
|
end = None
|
|
processed[nmix] = sources[:,:,start:end].copy()
|
|
|
|
sources = list(processed.values())
|
|
sources = np.concatenate(sources, axis=-1)
|
|
widget_text.write('Done!\n')
|
|
return sources
|
|
|
|
def demix_demucs_split(self, mix):
|
|
|
|
print('shift_set ', shift_set)
|
|
widget_text.write(base_text + "Split Mode is on. (Chunks disabled for Demucs Model)\n")
|
|
widget_text.write(base_text + "Running Demucs Inference...\n")
|
|
widget_text.write(base_text + "Processing "f"{len(mix)} slices... ")
|
|
print(' Running Demucs Inference...')
|
|
|
|
mix = torch.tensor(mix, dtype=torch.float32)
|
|
ref = mix.mean(0)
|
|
mix = (mix - ref.mean()) / ref.std()
|
|
|
|
with torch.no_grad():
|
|
sources = apply_model(self.demucs, mix[None], split=split_mode, device=device, overlap=overlap_set, shifts=shift_set, progress=False)[0]
|
|
|
|
widget_text.write('Done!\n')
|
|
|
|
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
|
|
sources[[0,1]] = sources[[1,0]]
|
|
return sources
|
|
|
|
data = {
|
|
# Paths
|
|
'input_paths': None,
|
|
'export_path': None,
|
|
'saveFormat': 'Wav',
|
|
# Processing Options
|
|
'demucsmodel': True,
|
|
'gpu': -1,
|
|
'chunks': 10,
|
|
'non_red': False,
|
|
'noisereduc_s': 3,
|
|
'modelFolder': False,
|
|
'voc_only': False,
|
|
'inst_only': False,
|
|
'n_fft_scale': 6144,
|
|
'dim_f': 2048,
|
|
'noise_pro_select': 'Auto Select',
|
|
'overlap': 0.5,
|
|
'shifts': 0,
|
|
'margin': 44100,
|
|
'split_mode': False,
|
|
'compensate': 1.03597672895,
|
|
'demucs_only': False,
|
|
'mixing': 'Default',
|
|
'DemucsModel_MDX': 'UVR_Demucs_Model_1',
|
|
# Choose Model
|
|
'mdxnetModel': 'UVR-MDX-NET 1',
|
|
'mdxnetModeltype': 'Vocals (Custom)',
|
|
}
|
|
default_chunks = data['chunks']
|
|
default_noisereduc_s = data['noisereduc_s']
|
|
|
|
def update_progress(progress_var, total_files, file_num, step: float = 1):
|
|
"""Calculate the progress for the progress widget in the GUI"""
|
|
base = (100 / total_files)
|
|
progress = base * (file_num - 1)
|
|
progress += base * step
|
|
|
|
progress_var.set(progress)
|
|
|
|
def get_baseText(total_files, file_num):
|
|
"""Create the base text for the command widget"""
|
|
text = 'File {file_num}/{total_files} '.format(file_num=file_num,
|
|
total_files=total_files)
|
|
return text
|
|
|
|
warnings.filterwarnings("ignore")
|
|
cpu = torch.device('cpu')
|
|
|
|
def hide_opt():
|
|
with open(os.devnull, "w") as devnull:
|
|
old_stdout = sys.stdout
|
|
sys.stdout = devnull
|
|
try:
|
|
yield
|
|
finally:
|
|
sys.stdout = old_stdout
|
|
|
|
def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress_var: tk.Variable,
|
|
**kwargs: dict):
|
|
|
|
global widget_text
|
|
global gui_progress_bar
|
|
global music_file
|
|
global default_chunks
|
|
global default_noisereduc_s
|
|
global _basename
|
|
global _mixture
|
|
global modeltype
|
|
global n_fft_scale_set
|
|
global dim_f_set
|
|
global progress_kwargs
|
|
global base_text
|
|
global model_set
|
|
global model_set_name
|
|
global stemset_n
|
|
global noise_pro_set
|
|
global demucs_model_set
|
|
|
|
global mdx_model_hash
|
|
|
|
global channel_set
|
|
global margin_set
|
|
global overlap_set
|
|
global shift_set
|
|
global source_val
|
|
global split_mode
|
|
|
|
global demucs_switch
|
|
|
|
# Update default settings
|
|
default_chunks = data['chunks']
|
|
default_noisereduc_s = data['noisereduc_s']
|
|
|
|
widget_text = text_widget
|
|
gui_progress_bar = progress_var
|
|
|
|
#Error Handling
|
|
|
|
onnxmissing = "[ONNXRuntimeError] : 3 : NO_SUCHFILE"
|
|
onnxmemerror = "onnxruntime::CudaCall CUDA failure 2: out of memory"
|
|
onnxmemerror2 = "onnxruntime::BFCArena::AllocateRawInternal"
|
|
systemmemerr = "DefaultCPUAllocator: not enough memory"
|
|
runtimeerr = "CUDNN error executing cudnnSetTensorNdDescriptor"
|
|
cuda_err = "CUDA out of memory"
|
|
mod_err = "ModuleNotFoundError"
|
|
file_err = "FileNotFoundError"
|
|
ffmp_err = """audioread\__init__.py", line 116, in audio_open"""
|
|
sf_write_err = "sf.write"
|
|
model_adv_set_err = "Got invalid dimensions for input"
|
|
|
|
try:
|
|
with open('errorlog.txt', 'w') as f:
|
|
f.write(f'No errors to report at this time.' + f'\n\nLast Process Method Used: MDX-Net' +
|
|
f'\nLast Conversion Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
|
|
except:
|
|
pass
|
|
|
|
data.update(kwargs)
|
|
|
|
if data['mdxnetModeltype'] == 'Vocals (Custom)':
|
|
stemset = 'v'
|
|
source_val_set = 3
|
|
stem_name = '(Vocals)'
|
|
if data['mdxnetModeltype'] == 'Other (Custom)':
|
|
stemset = 'o'
|
|
source_val_set = 2
|
|
stem_name = '(Other)'
|
|
if data['mdxnetModeltype'] == 'Drums (Custom)':
|
|
stemset = 'd'
|
|
source_val_set = 1
|
|
stem_name = '(Drums)'
|
|
if data['mdxnetModeltype'] == 'Bass (Custom)':
|
|
stemset = 'b'
|
|
source_val_set = 0
|
|
stem_name = '(Bass)'
|
|
if data['mdxnetModeltype'] == 'Vocals (Default)':
|
|
stemset = 'v'
|
|
source_val_set = 3
|
|
stem_name = '(Vocals)'
|
|
if data['mdxnetModeltype'] == 'Other (Default)':
|
|
stemset = 'o'
|
|
source_val_set = 2
|
|
stem_name = '(Other)'
|
|
if data['mdxnetModeltype'] == 'Drums (Default)':
|
|
stemset = 'd'
|
|
source_val_set = 1
|
|
stem_name = '(Drums)'
|
|
if data['mdxnetModeltype'] == 'Bass (Default)':
|
|
stemset = 'b'
|
|
source_val_set = 0
|
|
stem_name = '(Bass)'
|
|
|
|
if data['mdxnetModel'] == 'UVR-MDX-NET 1':
|
|
if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_1_9703.onnx'):
|
|
model_set = 'UVR_MDXNET_1_9703'
|
|
model_set_name = 'UVR_MDXNET_1_9703'
|
|
else:
|
|
model_set = 'UVR_MDXNET_9703'
|
|
model_set_name = 'UVR_MDXNET_9703'
|
|
modeltype = 'v'
|
|
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
|
stemset_n = '(Vocals)'
|
|
source_val = 3
|
|
n_fft_scale_set=6144
|
|
dim_f_set=2048
|
|
elif data['mdxnetModel'] == 'UVR-MDX-NET 2':
|
|
if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_2_9682.onnx'):
|
|
model_set = 'UVR_MDXNET_2_9682'
|
|
model_set_name = 'UVR_MDXNET_2_9682'
|
|
else:
|
|
model_set = 'UVR_MDXNET_9682'
|
|
model_set_name = 'UVR_MDXNET_9682'
|
|
modeltype = 'v'
|
|
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
|
stemset_n = '(Vocals)'
|
|
source_val = 3
|
|
n_fft_scale_set=6144
|
|
dim_f_set=2048
|
|
elif data['mdxnetModel'] == 'UVR-MDX-NET 3':
|
|
if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_3_9662.onnx'):
|
|
model_set = 'UVR_MDXNET_3_9662'
|
|
model_set_name = 'UVR_MDXNET_3_9662'
|
|
else:
|
|
model_set = 'UVR_MDXNET_9662'
|
|
model_set_name = 'UVR_MDXNET_9662'
|
|
modeltype = 'v'
|
|
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
|
stemset_n = '(Vocals)'
|
|
source_val = 3
|
|
n_fft_scale_set=6144
|
|
dim_f_set=2048
|
|
elif data['mdxnetModel'] == 'UVR-MDX-NET Karaoke':
|
|
model_set = 'UVR_MDXNET_KARA'
|
|
model_set_name = 'UVR_MDXNET_Karaoke'
|
|
modeltype = 'v'
|
|
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
|
|
stemset_n = '(Vocals)'
|
|
source_val = 3
|
|
n_fft_scale_set=6144
|
|
dim_f_set=2048
|
|
elif 'other' in data['mdxnetModel']:
|
|
model_set = 'other'
|
|
model_set_name = 'other'
|
|
modeltype = 'o'
|
|
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
|
|
stemset_n = '(Other)'
|
|
source_val = 2
|
|
n_fft_scale_set=8192
|
|
dim_f_set=2048
|
|
elif 'drums' in data['mdxnetModel']:
|
|
model_set = 'drums'
|
|
model_set_name = 'drums'
|
|
modeltype = 'd'
|
|
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
|
|
stemset_n = '(Drums)'
|
|
source_val = 1
|
|
n_fft_scale_set=4096
|
|
dim_f_set=2048
|
|
elif 'bass' in data['mdxnetModel']:
|
|
model_set = 'bass'
|
|
model_set_name = 'bass'
|
|
modeltype = 'b'
|
|
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
|
|
stemset_n = '(Bass)'
|
|
source_val = 0
|
|
n_fft_scale_set=16384
|
|
dim_f_set=2048
|
|
else:
|
|
model_set = data['mdxnetModel']
|
|
model_set_name = data['mdxnetModel']
|
|
modeltype = stemset
|
|
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
|
|
stemset_n = stem_name
|
|
source_val = source_val_set
|
|
n_fft_scale_set=int(data['n_fft_scale'])
|
|
dim_f_set=int(data['dim_f'])
|
|
|
|
|
|
if data['noise_pro_select'] == 'Auto Select':
|
|
noise_pro_set = noise_pro
|
|
else:
|
|
noise_pro_set = data['noise_pro_select']
|
|
|
|
|
|
print(n_fft_scale_set)
|
|
print(dim_f_set)
|
|
print(data['DemucsModel_MDX'])
|
|
|
|
|
|
stime = time.perf_counter()
|
|
progress_var.set(0)
|
|
text_widget.clear()
|
|
button_widget.configure(state=tk.DISABLED) # Disable Button
|
|
|
|
try: #Load File(s)
|
|
for file_num, music_file in tqdm(enumerate(data['input_paths'], start=1)):
|
|
|
|
overlap_set = float(data['overlap'])
|
|
channel_set = int(data['channel'])
|
|
margin_set = int(data['margin'])
|
|
shift_set = int(data['shifts'])
|
|
demucs_model_set = data['DemucsModel_MDX']
|
|
split_mode = data['split_mode']
|
|
demucs_switch = data['demucsmodel']
|
|
|
|
if stemset_n == '(Bass)':
|
|
if 'UVR' in demucs_model_set:
|
|
text_widget.write('The selected Demucs model can only be used with vocal stems.\n')
|
|
text_widget.write('Please select a 4 stem Demucs model and try again.\n\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
else:
|
|
pass
|
|
if stemset_n == '(Drums)':
|
|
if 'UVR' in demucs_model_set:
|
|
text_widget.write('The selected Demucs model can only be used with vocal stems.\n')
|
|
text_widget.write('Please select a 4 stem Demucs model and try again.\n\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
else:
|
|
pass
|
|
if stemset_n == '(Other)':
|
|
if 'UVR' in demucs_model_set:
|
|
text_widget.write('The selected Demucs model can only be used with vocal stems.\n')
|
|
text_widget.write('Please select a 4 stem Demucs model and try again.\n\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
else:
|
|
pass
|
|
|
|
_mixture = f'{data["input_paths"]}'
|
|
_basename = f'{data["export_path"]}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
|
|
|
|
# -Get text and update progress-
|
|
base_text = get_baseText(total_files=len(data['input_paths']),
|
|
file_num=file_num)
|
|
progress_kwargs = {'progress_var': progress_var,
|
|
'total_files': len(data['input_paths']),
|
|
'file_num': file_num}
|
|
|
|
print(model_set)
|
|
|
|
try:
|
|
|
|
if float(data['noisereduc_s']) >= 10:
|
|
text_widget.write('Error: Noise Reduction only supports values between 0-10.\nPlease set a value between 0-10 (with or without decimals) and try again.')
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
total, used, free = shutil.disk_usage("/")
|
|
|
|
|
|
total_space = int(total/1.074e+9)
|
|
used_space = int(used/1.074e+9)
|
|
free_space = int(free/1.074e+9)
|
|
|
|
|
|
if int(free/1.074e+9) <= int(2):
|
|
text_widget.write('Error: Not enough storage on main drive to continue. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly. \n\nPlease ensure your main drive has at least 3 GB\'s of storage and try again.\n\n')
|
|
text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n')
|
|
text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n')
|
|
text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n')
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
if int(free/1.074e+9) in [3, 4, 5, 6, 7, 8]:
|
|
text_widget.write('Warning: Your main drive is running low on storage. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly.\n\n')
|
|
text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n')
|
|
text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n')
|
|
text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n\n')
|
|
except:
|
|
pass
|
|
|
|
if data['noisereduc_s'] == 'None':
|
|
pass
|
|
else:
|
|
if not os.path.isfile("lib_v5\sox\sox.exe"):
|
|
data['noisereduc_s'] = 'None'
|
|
data['non_red'] = False
|
|
widget_text.write(base_text + 'SoX is missing and required for noise reduction.\n')
|
|
widget_text.write(base_text + 'See the \"More Info\" tab in the Help Guide.\n')
|
|
widget_text.write(base_text + 'Noise Reduction will be disabled until SoX is available.\n\n')
|
|
|
|
update_progress(**progress_kwargs,
|
|
step=0)
|
|
|
|
e = os.path.join(data["export_path"])
|
|
|
|
demucsmodel = 'models/Demucs_Models/' + str(data['DemucsModel_MDX'])
|
|
|
|
pred = Predictor()
|
|
pred.prediction_setup()
|
|
|
|
print(demucsmodel)
|
|
|
|
# split
|
|
pred.prediction(
|
|
m=music_file,
|
|
)
|
|
|
|
except Exception as e:
|
|
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
|
|
message = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n'
|
|
if runtimeerr in message:
|
|
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
|
|
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
|
|
text_widget.write(f'\nError Received:\n\n')
|
|
text_widget.write(f'Your PC cannot process this audio file with the chunk size selected.\nPlease lower the chunk size and try again.\n\n')
|
|
text_widget.write(f'If this error persists, please contact the developers.\n\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
try:
|
|
with open('errorlog.txt', 'w') as f:
|
|
f.write(f'Last Error Received:\n\n' +
|
|
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
|
|
f'Process Method: MDX-Net\n\n' +
|
|
f'Your PC cannot process this audio file with the chunk size selected.\nPlease lower the chunk size and try again.\n\n' +
|
|
f'If this error persists, please contact the developers.\n\n' +
|
|
f'Raw error details:\n\n' +
|
|
message + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
|
|
except:
|
|
pass
|
|
torch.cuda.empty_cache()
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
if cuda_err in message:
|
|
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
|
|
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
|
|
text_widget.write(f'\nError Received:\n\n')
|
|
text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n')
|
|
text_widget.write(f'Please close any GPU intensive applications and try again.\n')
|
|
text_widget.write(f'If the error persists, your GPU might not be supported.\n\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
try:
|
|
with open('errorlog.txt', 'w') as f:
|
|
f.write(f'Last Error Received:\n\n' +
|
|
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
|
|
f'Process Method: MDX-Net\n\n' +
|
|
f'The application was unable to allocate enough GPU memory to use this model.\n' +
|
|
f'Please close any GPU intensive applications and try again.\n' +
|
|
f'If the error persists, your GPU might not be supported.\n\n' +
|
|
f'Raw error details:\n\n' +
|
|
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
|
|
except:
|
|
pass
|
|
torch.cuda.empty_cache()
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
if mod_err in message:
|
|
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
|
|
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
|
|
text_widget.write(f'\nError Received:\n\n')
|
|
text_widget.write(f'Application files(s) are missing.\n')
|
|
text_widget.write("\n" + f'{type(e).__name__} - "{e}"' + "\n\n")
|
|
text_widget.write(f'Please check for missing files/scripts in the app directory and try again.\n')
|
|
text_widget.write(f'If the error persists, please reinstall application or contact the developers.\n\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
try:
|
|
with open('errorlog.txt', 'w') as f:
|
|
f.write(f'Last Error Received:\n\n' +
|
|
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
|
|
f'Process Method: MDX-Net\n\n' +
|
|
f'Application files(s) are missing.\n' +
|
|
f'Please check for missing files/scripts in the app directory and try again.\n' +
|
|
f'If the error persists, please reinstall application or contact the developers.\n\n' +
|
|
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
|
|
except:
|
|
pass
|
|
torch.cuda.empty_cache()
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
if file_err in message:
|
|
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
|
|
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
|
|
text_widget.write(f'\nError Received:\n\n')
|
|
text_widget.write(f'Missing file error raised.\n')
|
|
text_widget.write("\n" + f'{type(e).__name__} - "{e}"' + "\n\n")
|
|
text_widget.write("\n" + f'Please address the error and try again.' + "\n")
|
|
text_widget.write(f'If this error persists, please contact the developers.\n\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
torch.cuda.empty_cache()
|
|
try:
|
|
with open('errorlog.txt', 'w') as f:
|
|
f.write(f'Last Error Received:\n\n' +
|
|
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
|
|
f'Process Method: MDX-Net\n\n' +
|
|
f'Missing file error raised.\n' +
|
|
"\n" + f'Please address the error and try again.' + "\n" +
|
|
f'If this error persists, please contact the developers.\n\n' +
|
|
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
|
|
except:
|
|
pass
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
if ffmp_err in message:
|
|
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
|
|
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
|
|
text_widget.write(f'\nError Received:\n\n')
|
|
text_widget.write(f'The input file type is not supported or FFmpeg is missing.\n')
|
|
text_widget.write(f'Please select a file type supported by FFmpeg and try again.\n\n')
|
|
text_widget.write(f'If FFmpeg is missing or not installed, you will only be able to process \".wav\" files \nuntil it is available on this system.\n\n')
|
|
text_widget.write(f'See the \"More Info\" tab in the Help Guide.\n\n')
|
|
text_widget.write(f'If this error persists, please contact the developers.\n\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
torch.cuda.empty_cache()
|
|
try:
|
|
with open('errorlog.txt', 'w') as f:
|
|
f.write(f'Last Error Received:\n\n' +
|
|
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
|
|
f'Process Method: MDX-Net\n\n' +
|
|
f'The input file type is not supported or FFmpeg is missing.\nPlease select a file type supported by FFmpeg and try again.\n\n' +
|
|
f'If FFmpeg is missing or not installed, you will only be able to process \".wav\" files until it is available on this system.\n\n' +
|
|
f'See the \"More Info\" tab in the Help Guide.\n\n' +
|
|
f'If this error persists, please contact the developers.\n\n' +
|
|
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
|
|
except:
|
|
pass
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
if onnxmissing in message:
|
|
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
|
|
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
|
|
text_widget.write(f'\nError Received:\n\n')
|
|
text_widget.write(f'The application could not detect this MDX-Net model on your system.\n')
|
|
text_widget.write(f'Please make sure all the models are present in the correct directory.\n')
|
|
text_widget.write(f'If the error persists, please reinstall application or contact the developers.\n\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
try:
|
|
with open('errorlog.txt', 'w') as f:
|
|
f.write(f'Last Error Received:\n\n' +
|
|
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
|
|
f'Process Method: MDX-Net\n\n' +
|
|
f'The application could not detect this MDX-Net model on your system.\n' +
|
|
f'Please make sure all the models are present in the correct directory.\n' +
|
|
f'If the error persists, please reinstall application or contact the developers.\n\n' +
|
|
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
|
|
except:
|
|
pass
|
|
torch.cuda.empty_cache()
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
if onnxmemerror in message:
|
|
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
|
|
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
|
|
text_widget.write(f'\nError Received:\n\n')
|
|
text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n')
|
|
text_widget.write(f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n')
|
|
text_widget.write(f'If the error persists, your GPU might not be supported.\n\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
try:
|
|
with open('errorlog.txt', 'w') as f:
|
|
f.write(f'Last Error Received:\n\n' +
|
|
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
|
|
f'Process Method: MDX-Net\n\n' +
|
|
f'The application was unable to allocate enough GPU memory to use this model.\n' +
|
|
f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n' +
|
|
f'If the error persists, your GPU might not be supported.\n\n' +
|
|
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
|
|
except:
|
|
pass
|
|
torch.cuda.empty_cache()
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
if onnxmemerror2 in message:
|
|
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
|
|
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
|
|
text_widget.write(f'\nError Received:\n\n')
|
|
text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n')
|
|
text_widget.write(f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n')
|
|
text_widget.write(f'If the error persists, your GPU might not be supported.\n\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
try:
|
|
with open('errorlog.txt', 'w') as f:
|
|
f.write(f'Last Error Received:\n\n' +
|
|
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
|
|
f'Process Method: MDX-Net\n\n' +
|
|
f'The application was unable to allocate enough GPU memory to use this model.\n' +
|
|
f'Please do the following:\n\n1. Close any GPU intensive applications.\n2. Lower the set chunk size.\n3. Then try again.\n\n' +
|
|
f'If the error persists, your GPU might not be supported.\n\n' +
|
|
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
|
|
except:
|
|
pass
|
|
torch.cuda.empty_cache()
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
if sf_write_err in message:
|
|
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
|
|
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
|
|
text_widget.write(f'\nError Received:\n\n')
|
|
text_widget.write(f'Could not write audio file.\n')
|
|
text_widget.write(f'This could be due to low storage on target device or a system permissions issue.\n')
|
|
text_widget.write(f"\nFor raw error details, go to the Error Log tab in the Help Guide.\n")
|
|
text_widget.write(f'\nIf the error persists, please contact the developers.\n\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
try:
|
|
with open('errorlog.txt', 'w') as f:
|
|
f.write(f'Last Error Received:\n\n' +
|
|
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
|
|
f'Process Method: MDX-Net\n\n' +
|
|
f'Could not write audio file.\n' +
|
|
f'This could be due to low storage on target device or a system permissions issue.\n' +
|
|
f'If the error persists, please contact the developers.\n\n' +
|
|
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
|
|
except:
|
|
pass
|
|
torch.cuda.empty_cache()
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
if systemmemerr in message:
|
|
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
|
|
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
|
|
text_widget.write(f'\nError Received:\n\n')
|
|
text_widget.write(f'The application was unable to allocate enough system memory to use this \nmodel.\n\n')
|
|
text_widget.write(f'Please do the following:\n\n1. Restart this application.\n2. Ensure any CPU intensive applications are closed.\n3. Then try again.\n\n')
|
|
text_widget.write(f'Please Note: Intel Pentium and Intel Celeron processors do not work well with \nthis application.\n\n')
|
|
text_widget.write(f'If the error persists, the system may not have enough RAM, or your CPU might \nnot be supported.\n\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
try:
|
|
with open('errorlog.txt', 'w') as f:
|
|
f.write(f'Last Error Received:\n\n' +
|
|
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
|
|
f'Process Method: MDX-Net\n\n' +
|
|
f'The application was unable to allocate enough system memory to use this model.\n' +
|
|
f'Please do the following:\n\n1. Restart this application.\n2. Ensure any CPU intensive applications are closed.\n3. Then try again.\n\n' +
|
|
f'Please Note: Intel Pentium and Intel Celeron processors do not work well with this application.\n\n' +
|
|
f'If the error persists, the system may not have enough RAM, or your CPU might \nnot be supported.\n\n' +
|
|
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
|
|
except:
|
|
pass
|
|
torch.cuda.empty_cache()
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
if model_adv_set_err in message:
|
|
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
|
|
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
|
|
text_widget.write(f'\nError Received:\n\n')
|
|
text_widget.write(f'The current ONNX model settings are not compatible with the selected \nmodel.\n\n')
|
|
text_widget.write(f'Please re-configure the advanced ONNX model settings accordingly and try \nagain.\n\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
try:
|
|
with open('errorlog.txt', 'w') as f:
|
|
f.write(f'Last Error Received:\n\n' +
|
|
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
|
|
f'Process Method: MDX-Net\n\n' +
|
|
f'The current ONNX model settings are not compatible with the selected model.\n\n' +
|
|
f'Please re-configure the advanced ONNX model settings accordingly and try again.\n\n' +
|
|
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
|
|
except:
|
|
pass
|
|
torch.cuda.empty_cache()
|
|
progress_var.set(0)
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
|
|
print(traceback_text)
|
|
print(type(e).__name__, e)
|
|
print(message)
|
|
|
|
try:
|
|
with open('errorlog.txt', 'w') as f:
|
|
f.write(f'Last Error Received:\n\n' +
|
|
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
|
|
f'Process Method: MDX-Net\n\n' +
|
|
f'If this error persists, please contact the developers with the error details.\n\n' +
|
|
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
|
|
except:
|
|
tk.messagebox.showerror(master=window,
|
|
title='Error Details',
|
|
message=message)
|
|
progress_var.set(0)
|
|
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
|
|
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
|
|
text_widget.write(f'\nError Received:\n')
|
|
text_widget.write("\nFor raw error details, go to the Error Log tab in the Help Guide.\n")
|
|
text_widget.write("\n" + f'Please address the error and try again.' + "\n")
|
|
text_widget.write(f'If this error persists, please contact the developers with the error details.\n\n')
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
|
|
try:
|
|
torch.cuda.empty_cache()
|
|
except:
|
|
pass
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
return
|
|
|
|
progress_var.set(0)
|
|
|
|
text_widget.write(f'\nConversion(s) Completed!\n')
|
|
|
|
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') # nopep8
|
|
torch.cuda.empty_cache()
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button
|
|
|
|
if __name__ == '__main__':
|
|
start_time = time.time()
|
|
main()
|
|
print("Successfully completed music demixing.");print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
|
|
|