Cool_Tools/ultimatevocalremovergui
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ultimatevocalremovergui/inference_v5.py

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import os
import importlib
import pydub
import shutil
import hashlib
import cv2
import librosa
import math
import numpy as np
import soundfile as sf
from tqdm import tqdm
from lib_v5 import dataset
from lib_v5 import spec_utils
from lib_v5.model_param_init import ModelParameters
import torch
from datetime import datetime
# Command line text parsing and widget manipulation
from collections import defaultdict
import tkinter as tk
import traceback # Error Message Recent Calls
import time # Timer
class VocalRemover(object):
def __init__(self, data, text_widget: tk.Text):
self.data = data
self.text_widget = text_widget
self.models = defaultdict(lambda: None)
self.devices = defaultdict(lambda: None)
# self.offset = model.offset
data = {
# Paths
'input_paths': None,
'export_path': None,
'saveFormat': 'wav',
# Processing Options
'gpu': -1,
'postprocess': True,
'tta': True,
'output_image': True,
'voc_only': False,
'inst_only': False,
# Models
'instrumentalModel': None,
'useModel': None,
# Constants
'window_size': 512,
'agg': 10,
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'high_end_process': 'mirroring',
'ModelParams': 'Auto'
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}
default_window_size = data['window_size']
default_agg = data['agg']
def update_progress(progress_var, total_files, file_num, step: float = 1):
"""Calculate the progress for the progress widget in the GUI"""
base = (100 / total_files)
progress = base * (file_num - 1)
progress += base * step
progress_var.set(progress)
def get_baseText(total_files, file_num):
"""Create the base text for the command widget"""
text = 'File {file_num}/{total_files} '.format(file_num=file_num,
total_files=total_files)
return text
def determineModelFolderName():
"""
Determine the name that is used for the folder and appended
to the back of the music files
"""
modelFolderName = ''
if not data['modelFolder']:
# Model Test Mode not selected
return modelFolderName
# -Instrumental-
if os.path.isfile(data['instrumentalModel']):
modelFolderName += os.path.splitext(os.path.basename(data['instrumentalModel']))[0]
if modelFolderName:
modelFolderName = '/' + modelFolderName
return modelFolderName
def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress_var: tk.Variable,
**kwargs: dict):
global model_params_d
global nn_arch_sizes
global nn_architecture
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#Error Handling
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runtimeerr = "CUDNN error executing cudnnSetTensorNdDescriptor"
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systemmemerr = "DefaultCPUAllocator: not enough memory"
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cuda_err = "CUDA out of memory"
mod_err = "ModuleNotFoundError"
file_err = "FileNotFoundError"
ffmp_err = """audioread\__init__.py", line 116, in audio_open"""
sf_write_err = "sf.write"
try:
with open('errorlog.txt', 'w') as f:
f.write(f'No errors to report at this time.' + f'\n\nLast Process Method Used: VR Architecture' +
f'\nLast Conversion Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
nn_arch_sizes = [
31191, # default
33966, 123821, 123812, 537238 # custom
]
nn_architecture = list('{}KB'.format(s) for s in nn_arch_sizes)
def save_files(wav_instrument, wav_vocals):
"""Save output music files"""
vocal_name = '(Vocals)'
instrumental_name = '(Instrumental)'
save_path = os.path.dirname(base_name)
# Swap names if vocal model
VModel="Vocal"
if VModel in model_name:
# Reverse names
vocal_name, instrumental_name = instrumental_name, vocal_name
# Save Temp File
# For instrumental the instrumental is the temp file
# and for vocal the instrumental is the temp file due
# to reversement
sf.write(f'temp.wav',
wav_instrument, mp.param['sr'])
appendModelFolderName = modelFolderName.replace('/', '_')
# -Save files-
# Instrumental
if instrumental_name is not None:
if data['modelFolder']:
instrumental_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{instrumental_name}',)
instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{instrumental_name}',)
instrumental_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{instrumental_name}',)
else:
instrumental_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name=f'{os.path.basename(base_name)}_{instrumental_name}',)
instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name=f'{os.path.basename(base_name)}_{instrumental_name}',)
instrumental_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name=f'{os.path.basename(base_name)}_{instrumental_name}',)
if os.path.isfile(instrumental_path):
file_exists_i = 'there'
else:
file_exists_i = 'not_there'
if VModel in model_name and data['voc_only']:
sf.write(instrumental_path,
wav_instrument, mp.param['sr'])
elif VModel in model_name and data['inst_only']:
pass
elif data['voc_only']:
pass
else:
sf.write(instrumental_path,
wav_instrument, mp.param['sr'])
# Vocal
if vocal_name is not None:
if data['modelFolder']:
vocal_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{vocal_name}',)
vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{vocal_name}',)
vocal_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{vocal_name}',)
else:
vocal_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name=f'{os.path.basename(base_name)}_{vocal_name}',)
vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format(
save_path=save_path,
file_name=f'{os.path.basename(base_name)}_{vocal_name}',)
vocal_path_flac = '{save_path}/{file_name}.flac'.format(
save_path=save_path,
file_name=f'{os.path.basename(base_name)}_{vocal_name}',)
if os.path.isfile(vocal_path):
file_exists_v = 'there'
else:
file_exists_v = 'not_there'
if VModel in model_name and data['inst_only']:
sf.write(vocal_path,
wav_vocals, mp.param['sr'])
elif VModel in model_name and data['voc_only']:
pass
elif data['inst_only']:
pass
else:
sf.write(vocal_path,
wav_vocals, mp.param['sr'])
if data['saveFormat'] == 'Mp3':
try:
if data['inst_only'] == True:
pass
else:
musfile = pydub.AudioSegment.from_wav(vocal_path)
musfile.export(vocal_path_mp3, format="mp3", bitrate="320k")
if file_exists_v == 'there':
pass
else:
try:
os.remove(vocal_path)
except:
pass
if data['voc_only'] == True:
pass
else:
musfile = pydub.AudioSegment.from_wav(instrumental_path)
musfile.export(instrumental_path_mp3, format="mp3", bitrate="320k")
if file_exists_i == 'there':
pass
else:
try:
os.remove(instrumental_path)
except:
pass
except Exception as e:
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n'
if "ffmpeg" in errmessage:
text_widget.write(base_text + 'Failed to save output(s) as Mp3(s).\n')
text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n')
text_widget.write(base_text + 'Moving on...\n')
else:
text_widget.write(base_text + 'Failed to save output(s) as Mp3(s).\n')
text_widget.write(base_text + 'Please check error log.\n')
text_widget.write(base_text + 'Moving on...\n')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while attempting to save file as mp3 "{os.path.basename(music_file)}":\n' +
f'Process Method: VR Architecture\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 VModel in model_name:
if data['inst_only'] == True:
pass
else:
musfile = pydub.AudioSegment.from_wav(instrumental_path)
musfile.export(instrumental_path_flac, format="flac")
if file_exists_v == 'there':
pass
else:
try:
os.remove(instrumental_path)
except:
pass
if data['voc_only'] == True:
pass
else:
musfile = pydub.AudioSegment.from_wav(vocal_path)
musfile.export(vocal_path_flac, format="flac")
if file_exists_i == 'there':
pass
else:
try:
os.remove(vocal_path)
except:
pass
else:
if data['inst_only'] == 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'] == 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:
text_widget.write(base_text + 'Failed to save output(s) as Flac(s).\n')
text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n')
text_widget.write(base_text + 'Moving on...\n')
else:
text_widget.write(base_text + 'Failed to save output(s) as Flac(s).\n')
text_widget.write(base_text + 'Please check error log.\n')
text_widget.write(base_text + 'Moving on...\n')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while attempting to save file as flac "{os.path.basename(music_file)}":\n' +
f'Process Method: VR Architecture\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
data.update(kwargs)
# Update default settings
global default_window_size
global default_agg
default_window_size = data['window_size']
default_agg = data['agg']
stime = time.perf_counter()
progress_var.set(0)
text_widget.clear()
button_widget.configure(state=tk.DISABLED) # Disable Button
vocal_remover = VocalRemover(data, text_widget)
modelFolderName = determineModelFolderName()
# Separation Preperation
try: #Load File(s)
for file_num, music_file in enumerate(data['input_paths'], start=1):
# Determine File Name
base_name = f'{data["export_path"]}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
model_name = os.path.basename(data[f'{data["useModel"]}Model'])
model = vocal_remover.models[data['useModel']]
device = vocal_remover.devices[data['useModel']]
# -Get text and update progress-
base_text = get_baseText(total_files=len(data['input_paths']),
file_num=file_num)
progress_kwargs = {'progress_var': progress_var,
'total_files': len(data['input_paths']),
'file_num': file_num}
update_progress(**progress_kwargs,
step=0)
try:
total, used, free = shutil.disk_usage("/")
total_space = int(total/1.074e+9)
used_space = int(used/1.074e+9)
free_space = int(free/1.074e+9)
if int(free/1.074e+9) <= int(2):
text_widget.write('Error: Not enough storage on main drive to continue. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly. \n\nPlease ensure your main drive has at least 3 GB\'s of storage and try again.\n\n')
text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n')
text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n')
text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n')
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if int(free/1.074e+9) in [3, 4, 5, 6, 7, 8]:
text_widget.write('Warning: Your main drive is running low on storage. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly.\n\n')
text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n')
text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n')
text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n\n')
except:
pass
#Load Model
text_widget.write(base_text + 'Loading models...')
model_size = math.ceil(os.stat(data['instrumentalModel']).st_size / 1024)
nn_architecture = '{}KB'.format(min(nn_arch_sizes, key=lambda x:abs(x-model_size)))
nets = importlib.import_module('lib_v5.nets' + f'_{nn_architecture}'.replace('_{}KB'.format(nn_arch_sizes[0]), ''), package=None)
aggresive_set = float(data['agg']/100)
ModelName=(data['instrumentalModel'])
#Package Models
model_hash = hashlib.md5(open(ModelName,'rb').read()).hexdigest()
print(model_hash)
#v5 Models
if model_hash == '47939caf0cfe52a0e81442b85b971dfd':
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model_params_auto=str('lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100')
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if model_hash == '4e4ecb9764c50a8c414fee6e10395bbe':
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model_params_auto=str('lib_v5/modelparams/4band_v2.json')
param_name_auto=str('4band_v2')
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if model_hash == 'e60a1e84803ce4efc0a6551206cc4b71':
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model_params_auto=str('lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100')
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if model_hash == 'a82f14e75892e55e994376edbf0c8435':
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model_params_auto=str('lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100')
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if model_hash == '6dd9eaa6f0420af9f1d403aaafa4cc06':
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model_params_auto=str('lib_v5/modelparams/4band_v2_sn.json')
param_name_auto=str('4band_v2_sn')
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if model_hash == '5c7bbca45a187e81abbbd351606164e5':
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model_params_auto=str('lib_v5/modelparams/3band_44100_msb2.json')
param_name_auto=str('3band_44100_msb2')
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if model_hash == 'd6b2cb685a058a091e5e7098192d3233':
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model_params_auto=str('lib_v5/modelparams/3band_44100_msb2.json')
param_name_auto=str('3band_44100_msb2')
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if model_hash == 'c1b9f38170a7c90e96f027992eb7c62b':
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model_params_auto=str('lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100')
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if model_hash == 'c3448ec923fa0edf3d03a19e633faa53':
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model_params_auto=str('lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100')
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if model_hash == '68aa2c8093d0080704b200d140f59e54':
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model_params_auto=str('lib_v5/modelparams/3band_44100.json')
param_name_auto=str('3band_44100.json')
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if model_hash == 'fdc83be5b798e4bd29fe00fe6600e147':
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model_params_auto=str('lib_v5/modelparams/3band_44100_mid.json')
param_name_auto=str('3band_44100_mid.json')
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if model_hash == '2ce34bc92fd57f55db16b7a4def3d745':
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model_params_auto=str('lib_v5/modelparams/3band_44100_mid.json')
param_name_auto=str('3band_44100_mid.json')
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if model_hash == '52fdca89576f06cf4340b74a4730ee5f':
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model_params_auto=str('lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100.json')
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if model_hash == '41191165b05d38fc77f072fa9e8e8a30':
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model_params_auto=str('lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100.json')
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if model_hash == '89e83b511ad474592689e562d5b1f80e':
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model_params_auto=str('lib_v5/modelparams/2band_32000.json')
param_name_auto=str('2band_32000.json')
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if model_hash == '0b954da81d453b716b114d6d7c95177f':
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model_params_auto=str('lib_v5/modelparams/2band_32000.json')
param_name_auto=str('2band_32000.json')
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#v4 Models
if model_hash == '6a00461c51c2920fd68937d4609ed6c8':
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model_params_auto=str('lib_v5/modelparams/1band_sr16000_hl512.json')
param_name_auto=str('1band_sr16000_hl512')
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if model_hash == '0ab504864d20f1bd378fe9c81ef37140':
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model_params_auto=str('lib_v5/modelparams/1band_sr32000_hl512.json')
param_name_auto=str('1band_sr32000_hl512')
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if model_hash == '7dd21065bf91c10f7fccb57d7d83b07f':
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model_params_auto=str('lib_v5/modelparams/1band_sr32000_hl512.json')
param_name_auto=str('1band_sr32000_hl512')
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if model_hash == '80ab74d65e515caa3622728d2de07d23':
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model_params_auto=str('lib_v5/modelparams/1band_sr32000_hl512.json')
param_name_auto=str('1band_sr32000_hl512')
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if model_hash == 'edc115e7fc523245062200c00caa847f':
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model_params_auto=str('lib_v5/modelparams/1band_sr33075_hl384.json')
param_name_auto=str('1band_sr33075_hl384')
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if model_hash == '28063e9f6ab5b341c5f6d3c67f2045b7':
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model_params_auto=str('lib_v5/modelparams/1band_sr33075_hl384.json')
param_name_auto=str('1band_sr33075_hl384')
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if model_hash == 'b58090534c52cbc3e9b5104bad666ef2':
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model_params_auto=str('lib_v5/modelparams/1band_sr44100_hl512.json')
param_name_auto=str('1band_sr44100_hl512')
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if model_hash == '0cdab9947f1b0928705f518f3c78ea8f':
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model_params_auto=str('lib_v5/modelparams/1band_sr44100_hl512.json')
param_name_auto=str('1band_sr44100_hl512')
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if model_hash == 'ae702fed0238afb5346db8356fe25f13':
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model_params_auto=str('lib_v5/modelparams/1band_sr44100_hl1024.json')
param_name_auto=str('1band_sr44100_hl1024')
Add files via upload
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#User Models
#1 Band
if '1band_sr16000_hl512' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/1band_sr16000_hl512.json')
param_name_auto=str('1band_sr16000_hl512')
Add files via upload
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if '1band_sr32000_hl512' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/1band_sr32000_hl512.json')
param_name_auto=str('1band_sr32000_hl512')
Add files via upload
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if '1band_sr33075_hl384' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/1band_sr33075_hl384.json')
param_name_auto=str('1band_sr33075_hl384')
Add files via upload
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if '1band_sr44100_hl256' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/1band_sr44100_hl256.json')
param_name_auto=str('1band_sr44100_hl256')
Add files via upload
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if '1band_sr44100_hl512' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/1band_sr44100_hl512.json')
param_name_auto=str('1band_sr44100_hl512')
Add files via upload
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if '1band_sr44100_hl1024' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/1band_sr44100_hl1024.json')
param_name_auto=str('1band_sr44100_hl1024')
Add files via upload
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#2 Band
if '2band_44100_lofi' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/2band_44100_lofi.json')
param_name_auto=str('2band_44100_lofi')
Add files via upload
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if '2band_32000' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/2band_32000.json')
param_name_auto=str('2band_32000')
Add files via upload
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if '2band_48000' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/2band_48000.json')
param_name_auto=str('2band_48000')
Add files via upload
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#3 Band
if '3band_44100' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/3band_44100.json')
param_name_auto=str('3band_44100')
Add files via upload
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if '3band_44100_mid' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/3band_44100_mid.json')
param_name_auto=str('3band_44100_mid')
Add files via upload
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if '3band_44100_msb2' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/3band_44100_msb2.json')
param_name_auto=str('3band_44100_msb2')
Add files via upload
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#4 Band
if '4band_44100' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100')
Add files via upload
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if '4band_44100_mid' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/4band_44100_mid.json')
param_name_auto=str('4band_44100_mid')
Add files via upload
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if '4band_44100_msb' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/4band_44100_msb.json')
param_name_auto=str('4band_44100_msb')
Add files via upload
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if '4band_44100_msb2' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/4band_44100_msb2.json')
param_name_auto=str('4band_44100_msb2')
Add files via upload
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if '4band_44100_reverse' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/4band_44100_reverse.json')
param_name_auto=str('4band_44100_reverse')
Add files via upload
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if '4band_44100_sw' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/4band_44100_sw.json')
param_name_auto=str('4band_44100_sw')
Add files via upload
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if '4band_v2' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/4band_v2.json')
param_name_auto=str('4band_v2')
Add files via upload
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if '4band_v2_sn' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/4band_v2_sn.json')
param_name_auto=str('4band_v2_sn')
Add files via upload
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if 'tmodelparam' in ModelName:
Add files via upload
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model_params_auto=str('lib_v5/modelparams/tmodelparam.json')
param_name_auto=str('User Model Param Set')
Add files via upload
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text_widget.write(' Done!\n')
Add files via upload
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if data['ModelParams'] == 'Auto':
param_name = param_name_auto
model_params_d = model_params_auto
else:
param_name = str(data['ModelParams'])
model_params_d = str('lib_v5/modelparams/' + data['ModelParams'])
Add files via upload
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try:
print('Model Parameters:', model_params_d)
text_widget.write(base_text + 'Loading assigned model parameters ' + '\"' + param_name + '\"... ')
except Exception as e:
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n'
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'Model parameters are missing.\n\n')
text_widget.write(f'Please check the following:\n')
text_widget.write(f'1. Make sure the model is still present.\n')
text_widget.write(f'2. If you are running a model that was not originally included in this package, \nplease append the modelparam name to the model name.\n')
text_widget.write(f' - Example if using \"4band_v2.json\" modelparam: \"model_4band_v2.pth\"\n\n')
text_widget.write(f'Please address this and try again.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: VR Architecture\n\n' +
f'Model parameters are missing.\n\n' +
f'Please check the following:\n' +
f'1. Make sure the model is still present.\n' +
f'2. If you are running a model that was not originally included in this package, please append the modelparam name to the model name.\n' +
f' - Example if using \"4band_v2.json\" modelparam: \"model_4band_v2.pth\"\n\n' +
f'Please address this and try again.\n\n' +
f'Raw error details:\n\n' +
errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
Add files via upload
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Add files via upload
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mp = ModelParameters(model_params_d)
text_widget.write('Done!\n')
# -Instrumental-
if os.path.isfile(data['instrumentalModel']):
device = torch.device('cpu')
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
model.load_state_dict(torch.load(data['instrumentalModel'],
map_location=device))
if torch.cuda.is_available() and data['gpu'] >= 0:
device = torch.device('cuda:{}'.format(data['gpu']))
model.to(device)
vocal_remover.models['instrumental'] = model
vocal_remover.devices['instrumental'] = device
model_name = os.path.basename(data[f'{data["useModel"]}Model'])
mp = ModelParameters(model_params_d)
# -Go through the different steps of seperation-
# Wave source
text_widget.write(base_text + 'Loading audio source...')
X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
bands_n = len(mp.param['band'])
for d in range(bands_n, 0, -1):
bp = mp.param['band'][d]
if d == bands_n: # high-end band
X_wave[d], _ = librosa.load(
music_file, bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
if X_wave[d].ndim == 1:
X_wave[d] = np.asarray([X_wave[d], X_wave[d]])
else: # lower bands
X_wave[d] = librosa.resample(X_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
# Stft of wave source
X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'],
mp.param['mid_side_b2'], mp.param['reverse'])
if d == bands_n and data['high_end_process'] != 'none':
input_high_end_h = (bp['n_fft']//2 - bp['crop_stop']) + (mp.param['pre_filter_stop'] - mp.param['pre_filter_start'])
input_high_end = X_spec_s[d][:, bp['n_fft']//2-input_high_end_h:bp['n_fft']//2, :]
text_widget.write('Done!\n')
update_progress(**progress_kwargs,
step=0.1)
text_widget.write(base_text + 'Loading the stft of audio source...')
text_widget.write(' Done!\n')
text_widget.write(base_text + "Please Wait...\n")
X_spec_m = spec_utils.combine_spectrograms(X_spec_s, mp)
del X_wave, X_spec_s
def inference(X_spec, device, model, aggressiveness):
def _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness):
model.eval()
with torch.no_grad():
preds = []
iterations = [n_window]
total_iterations = sum(iterations)
text_widget.write(base_text + "Processing "f"{total_iterations} Slices... ")
for i in tqdm(range(n_window)):
update_progress(**progress_kwargs,
step=(0.1 + (0.8/n_window * i)))
start = i * roi_size
X_mag_window = X_mag_pad[None, :, :, start:start + data['window_size']]
X_mag_window = torch.from_numpy(X_mag_window).to(device)
pred = model.predict(X_mag_window, aggressiveness)
pred = pred.detach().cpu().numpy()
preds.append(pred[0])
pred = np.concatenate(preds, axis=2)
text_widget.write('Done!\n')
return pred
def preprocess(X_spec):
X_mag = np.abs(X_spec)
X_phase = np.angle(X_spec)
return X_mag, X_phase
X_mag, X_phase = preprocess(X_spec)
coef = X_mag.max()
X_mag_pre = X_mag / coef
n_frame = X_mag_pre.shape[2]
pad_l, pad_r, roi_size = dataset.make_padding(n_frame,
data['window_size'], model.offset)
n_window = int(np.ceil(n_frame / roi_size))
X_mag_pad = np.pad(
X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
pred = _execute(X_mag_pad, roi_size, n_window,
device, model, aggressiveness)
pred = pred[:, :, :n_frame]
if data['tta']:
pad_l += roi_size // 2
pad_r += roi_size // 2
n_window += 1
X_mag_pad = np.pad(
X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
pred_tta = _execute(X_mag_pad, roi_size, n_window,
device, model, aggressiveness)
pred_tta = pred_tta[:, :, roi_size // 2:]
pred_tta = pred_tta[:, :, :n_frame]
return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.j * X_phase)
else:
return pred * coef, X_mag, np.exp(1.j * X_phase)
aggressiveness = {'value': aggresive_set, 'split_bin': mp.param['band'][1]['crop_stop']}
if data['tta']:
text_widget.write(base_text + "Running Inferences (TTA)...\n")
else:
text_widget.write(base_text + "Running Inference...\n")
pred, X_mag, X_phase = inference(X_spec_m,
device,
model, aggressiveness)
update_progress(**progress_kwargs,
step=0.9)
# Postprocess
if data['postprocess']:
try:
text_widget.write(base_text + 'Post processing...')
pred_inv = np.clip(X_mag - pred, 0, np.inf)
pred = spec_utils.mask_silence(pred, pred_inv)
text_widget.write(' Done!\n')
except Exception as e:
text_widget.write('\n' + base_text + 'Post process failed, check error log.\n')
text_widget.write(base_text + 'Moving on...\n')
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n'
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while attempting to run Post Processing on "{os.path.basename(music_file)}":\n' +
f'Process Method: VR Architecture\n\n' +
f'If this error persists, please contact the developers.\n\n' +
f'Raw error details:\n\n' +
errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
update_progress(**progress_kwargs,
step=0.95)
# Inverse stft
y_spec_m = pred * X_phase
v_spec_m = X_spec_m - y_spec_m
if data['voc_only'] and not data['inst_only']:
pass
else:
text_widget.write(base_text + 'Saving Instrumental... ')
if data['high_end_process'].startswith('mirroring'):
input_high_end_ = spec_utils.mirroring(data['high_end_process'], y_spec_m, input_high_end, mp)
wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp, input_high_end_h, input_high_end_)
if data['voc_only'] and not data['inst_only']:
pass
else:
text_widget.write('Done!\n')
else:
wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp)
if data['voc_only'] and not data['inst_only']:
pass
else:
text_widget.write('Done!\n')
if data['inst_only'] and not data['voc_only']:
pass
else:
text_widget.write(base_text + 'Saving Vocals... ')
if data['high_end_process'].startswith('mirroring'):
input_high_end_ = spec_utils.mirroring(data['high_end_process'], v_spec_m, input_high_end, mp)
wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp, input_high_end_h, input_high_end_)
if data['inst_only'] and not data['voc_only']:
pass
else:
text_widget.write('Done!\n')
else:
wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp)
if data['inst_only'] and not data['voc_only']:
pass
else:
text_widget.write('Done!\n')
update_progress(**progress_kwargs,
step=1)
# Save output music files
save_files(wav_instrument, wav_vocals)
update_progress(**progress_kwargs,
step=1)
# Save output image
if data['output_image']:
with open('{}_Instruments.jpg'.format(base_name), mode='wb') as f:
image = spec_utils.spectrogram_to_image(y_spec_m)
_, bin_image = cv2.imencode('.jpg', image)
bin_image.tofile(f)
with open('{}_Vocals.jpg'.format(base_name), mode='wb') as f:
image = spec_utils.spectrogram_to_image(v_spec_m)
_, bin_image = cv2.imencode('.jpg', image)
bin_image.tofile(f)
text_widget.write(base_text + 'Completed Seperation!\n\n')
except Exception as e:
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
message = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n'
if runtimeerr in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'Your PC cannot process this audio file with the chunk size selected.\nPlease lower the chunk size and try again.\n\n')
text_widget.write(f'If this error persists, please contact the developers.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: VR Architecture\n\n' +
f'Your PC cannot process this audio file with the chunk size selected.\nPlease lower the chunk size and try again.\n\n' +
f'If this error persists, please contact the developers.\n\n' +
f'Raw error details:\n\n' +
message + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if cuda_err in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n')
text_widget.write(f'Please close any GPU intensive applications and try again.\n')
text_widget.write(f'If the error persists, your GPU might not be supported.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: VR Architecture\n\n' +
f'The application was unable to allocate enough GPU memory to use this model.\n' +
f'Please close any GPU intensive applications and try again.\n' +
f'If the error persists, your GPU might not be supported.\n\n' +
f'Raw error details:\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if mod_err in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'Application files(s) are missing.\n')
text_widget.write("\n" + f'{type(e).__name__} - "{e}"' + "\n\n")
text_widget.write(f'Please check for missing files/scripts in the app directory and try again.\n')
text_widget.write(f'If the error persists, please reinstall application or contact the developers.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: VR Architecture\n\n' +
f'Application files(s) are missing.\n' +
f'Please check for missing files/scripts in the app directory and try again.\n' +
f'If the error persists, please reinstall application or contact the developers.\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if file_err in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'Missing file error raised.\n')
text_widget.write("\n" + f'{type(e).__name__} - "{e}"' + "\n\n")
text_widget.write("\n" + f'Please address the error and try again.' + "\n")
text_widget.write(f'If this error persists, please contact the developers.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
torch.cuda.empty_cache()
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: VR Architecture\n\n' +
f'Missing file error raised.\n' +
"\n" + f'Please address the error and try again.' + "\n" +
f'If this error persists, please contact the developers.\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if ffmp_err in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
Add files via upload
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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')
Add files via upload
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text_widget.write(f'See the \"More Info\" tab in the Help Guide.\n\n')
text_widget.write(f'If this error persists, please contact the developers.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
torch.cuda.empty_cache()
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: VR Architecture\n\n' +
Add files via upload
2022-05-11 08:42:20 +02:00
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' +
Add files via upload
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f'See the \"More Info\" tab in the Help Guide.\n\n' +
f'If this error persists, please contact the developers.\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if sf_write_err in message:
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n\n')
text_widget.write(f'Could not write audio file.\n')
text_widget.write(f'This could be due to low storage on target device or a system permissions issue.\n')
text_widget.write(f"\nFor raw error details, go to the Error Log tab in the Help Guide.\n")
text_widget.write(f'\nIf the error persists, please contact the developers.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
Add files via upload
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f'Process Method: VR Architecture\n\n' +
Add files via upload
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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)
Add files via upload
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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' +
Add files via upload
2022-06-13 09:07:19 +02:00
f'Process Method: VR Architecture\n\n' +
Add files via upload
2022-05-23 04:47:47 +02:00
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)
Add files via upload
2022-05-11 02:11:40 +02:00
button_widget.configure(state=tk.NORMAL) # Enable Button
return
print(traceback_text)
print(type(e).__name__, e)
print(message)
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while processing "{os.path.basename(music_file)}":\n' +
f'Process Method: VR Architecture\n\n' +
f'If this error persists, please contact the developers with the error details.\n\n' +
message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
tk.messagebox.showerror(master=window,
title='Error Details',
message=message)
progress_var.set(0)
text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n')
text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n')
text_widget.write(f'\nError Received:\n')
text_widget.write("\nFor raw error details, go to the Error Log tab in the Help Guide.\n")
text_widget.write("\n" + f'Please address the error and try again.' + "\n")
text_widget.write(f'If this error persists, please contact the developers with the error details.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
torch.cuda.empty_cache()
button_widget.configure(state=tk.NORMAL) # Enable Button
return
try:
os.remove('temp.wav')
except:
pass
progress_var.set(0)
text_widget.write(f'Conversion(s) Completed!\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') # nopep8
torch.cuda.empty_cache()
button_widget.configure(state=tk.NORMAL) # Enable Button
Reference in New Issue Copy Permalink