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inference_v5.py
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from collections import defaultdict
from datetime import datetime
from demucs.apply import BagOfModels, apply_model
from demucs.hdemucs import HDemucs
from demucs.pretrained import get_model as _gm
from lib_v5 import dataset
from lib_v5 import spec_utils
from lib_v5.model_param_init import ModelParameters
from models import stft, istft
from pathlib import Path
from random import randrange
from tqdm import tqdm
from tkinter import filedialog
import lib_v5.filelist
import cv2
import hashlib
import importlib
import librosa
import math
import numpy as np
import os
import pydub
import shutil
import soundfile as sf
import time # Timer
import tkinter as tk
import torch
import traceback # Error Message Recent Calls
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 = {
'agg': 10,
'demucsmodel_sel_VR': 'UVR_Demucs_Model_1',
'demucsmodelVR': True,
'export_path': None,
'gpu': -1,
'high_end_process': 'mirroring',
'input_paths': None,
'inst_only': False,
'instrumentalModel': None,
'ModelParams': 'Auto',
'mp3bit': '320k',
'normalize': False,
'output_image': True,
'overlap': 0.5,
'postprocess': True,
'saveFormat': 'wav',
'segment': 'None',
'settest': False,
'shifts': 0,
'split_mode': False,
'tta': True,
'useModel': None,
'voc_only': False,
'wavtype': 'PCM_16',
'window_size': 512,
}
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 gui_progress_bar
global nn_arch_sizes
global nn_architecture
global overlap_set
global shift_set
global split_mode
global demucs_model_set
global wav_type_set
global flac_type_set
global mp3_bit_set
global space
wav_type_set = data['wavtype']
gui_progress_bar = progress_var
#Error Handling
runtimeerr = "CUDNN error executing cudnnSetTensorNdDescriptor"
systemmemerr = "DefaultCPUAllocator: not enough memory"
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"
demucs_model_missing_err = "is neither a single pre-trained model or a bag of models."
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, 129605, 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
if data['demucsmodelVR']:
samplerate = 44100
else:
samplerate = mp.param['sr']
sf.write(f'temp.wav',
normalization_set(wav_instrument).T, samplerate, subtype=wav_type_set)
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,
normalization_set(wav_instrument).T, samplerate, subtype=wav_type_set)
elif VModel in model_name and data['inst_only']:
pass
elif data['voc_only']:
pass
else:
sf.write(instrumental_path,
normalization_set(wav_instrument).T, samplerate, subtype=wav_type_set)
# 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,
normalization_set(wav_vocals).T, samplerate, subtype=wav_type_set)
elif VModel in model_name and data['voc_only']:
pass
elif data['inst_only']:
pass
else:
sf.write(vocal_path,
normalization_set(wav_vocals).T, samplerate, subtype=wav_type_set)
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=mp3_bit_set)
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=mp3_bit_set)
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
global normalization_set
global update_prog
update_prog = update_progress
default_window_size = data['window_size']
default_agg = data['agg']
space = ' '*90
stime = time.perf_counter()
progress_var.set(0)
text_widget.clear()
button_widget.configure(state=tk.DISABLED) # Disable Button
overlap_set = float(data['overlap'])
shift_set = int(data['shifts'])
demucs_model_set = data['demucsmodel_sel_VR']
split_mode = data['split_mode']
if data['wavtype'] == '32-bit Float':
wav_type_set = 'FLOAT'
elif data['wavtype'] == '64-bit Float':
wav_type_set = 'DOUBLE'
else:
wav_type_set = data['wavtype']
flac_type_set = data['flactype']
mp3_bit_set = data['mp3bit']
if data['normalize'] == True:
normalization_set = spec_utils.normalize
print('normalization on')
else:
normalization_set = spec_utils.nonormalize
print('normalization off')
vocal_remover = VocalRemover(data, text_widget)
modelFolderName = determineModelFolderName()
timestampnum = round(datetime.utcnow().timestamp())
randomnum = randrange(100000, 1000000)
# Separation Preperation
try: #Load File(s)
for file_num, music_file in enumerate(data['input_paths'], start=1):
# Determine File Name
m=music_file
if data['settest']:
try:
base_name = f'{data["export_path"]}/{str(timestampnum)}_{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
except:
base_name = f'{data["export_path"]}/{str(randomnum)}_{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
else:
base_name = f'{data["export_path"]}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
global inference_type
inference_type = 'inference_vr'
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}
progress_demucs_kwargs = {'total_files': len(data['input_paths']),
'file_num': file_num, 'inference_type': inference_type}
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
if data['wavtype'] == '64-bit Float':
if data['saveFormat'] == 'Flac':
text_widget.write('Please select \"WAV\" as your save format to use 64-bit Float.\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
if data['wavtype'] == '64-bit Float':
if data['saveFormat'] == 'Mp3':
text_widget.write('Please select \"WAV\" as your save format to use 64-bit Float.\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
#Load Model
text_widget.write(base_text + 'Loading model...')
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
text_widget.write('Done!\n')
if data['ModelParams'] == 'Auto':
model_hash = hashlib.md5(open(ModelName,'rb').read()).hexdigest()
model_params = []
model_params = lib_v5.filelist.provide_model_param_hash(model_hash)
#print(model_params)
if model_params[0] == 'Not Found Using Hash':
model_params = []
model_params = lib_v5.filelist.provide_model_param_name(ModelName)
if model_params[0] == 'Not Found Using Name':
text_widget.write(base_text + f'Unable to set model parameters automatically with the selected model.\n')
confirm = tk.messagebox.askyesno(title='Unrecognized Model Detected',
message=f'\nThe application could not automatically set the model param for the selected model.\n\n' +
f'Would you like to select the model param file for this model?\n\n')
if confirm:
model_param_selection = filedialog.askopenfilename(initialdir='lib_v5/modelparams',
title=f'Select Model Param',
filetypes=[("Model Param", "*.json")])
model_param_file_path = str(model_param_selection)
model_param_file = os.path.splitext(os.path.basename(model_param_file_path))[0] + '.json'
model_params = [model_param_file_path, model_param_file]
with open(f"lib_v5/filelists/model_cache/vr_param_cache/{model_hash}.txt", 'w') as f:
f.write(model_param_file)
if model_params[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\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)))}')
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
else:
pass
else:
text_widget.write(base_text + f'Model param not selected.\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)))}')
torch.cuda.empty_cache()
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
else:
param = data['ModelParams']
model_param_file_path = f'lib_v5/modelparams/{param}'
model_params = [model_param_file_path, param]
text_widget.write(base_text + 'Loading assigned model parameters ' + '\"' + model_params[1] + '\"... ')
mp = ModelParameters(model_params[0])
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'])
# -Go through the different steps of Separation-
# 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')
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, tta=False):
model.eval()
global active_iterations
global progress_value
with torch.no_grad():
preds = []
iterations = [n_window]
if data['tta']:
total_iterations = sum(iterations)
total_iterations = total_iterations*2
else:
total_iterations = sum(iterations)
if tta:
active_iterations = sum(iterations)
active_iterations = active_iterations - 2
total_iterations = total_iterations - 2
else:
active_iterations = 0
progress_bar = 0
for i in range(n_window):
active_iterations += 1
if data['demucsmodelVR']:
update_progress(**progress_kwargs,
step=(0.1 + (0.5/total_iterations * active_iterations)))
else:
update_progress(**progress_kwargs,
step=(0.1 + (0.8/total_iterations * active_iterations)))
start = i * roi_size
progress_bar += 100
progress_value = progress_bar
active_iterations_step = active_iterations*100
step = (active_iterations_step / total_iterations)
percent_prog = f"{base_text}Inference Progress: {active_iterations}/{total_iterations} | {round(step)}%"
text_widget.percentage(percent_prog)
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)
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, tta=True)
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 + f"Running Inferences (TTA)... {space}\n")
else:
text_widget.write(base_text + f"Running Inference... {space}\n")
pred, X_mag, X_phase = inference(X_spec_m,
device,
model, aggressiveness)
text_widget.write('\n')
if data['postprocess']:
try:
text_widget.write(base_text + 'Post processing...')
pred_inv = np.clip(X_mag - pred, 0, np.inf)
pred = spec_utils.mask_silence(pred, pred_inv)
text_widget.write(' Done!\n')
except Exception as e:
text_widget.write('\n' + base_text + 'Post process failed, check error log.\n')
text_widget.write(base_text + 'Moving on...\n')
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n'
try:
with open('errorlog.txt', 'w') as f:
f.write(f'Last Error Received:\n\n' +
f'Error Received while attempting to run Post Processing on "{os.path.basename(music_file)}":\n' +
f'Process Method: VR Architecture\n\n' +
f'If this error persists, please contact the developers.\n\n' +
f'Raw error details:\n\n' +
errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
update_progress(**progress_kwargs,
step=0.95)
# Inverse stft
y_spec_m = pred * X_phase
v_spec_m = X_spec_m - y_spec_m
def demix_demucs(mix):
print(' Running Demucs Inference...')
if split_mode:
text_widget.write(base_text + f'Running Demucs Inference... {space}')
else:
text_widget.write(base_text + f'Running Demucs Inference... ')
mix = torch.tensor(mix, dtype=torch.float32)
ref = mix.mean(0)
mix = (mix - ref.mean()) / ref.std()
widget_text = text_widget
with torch.no_grad():
sources = apply_model(demucs,
mix[None],
gui_progress_bar,
widget_text,
update_prog,
split=split_mode,
device=device,
overlap=overlap_set,
shifts=shift_set,
progress=False,
segmen=True,
**progress_demucs_kwargs)[0]
if split_mode:
text_widget.write('\n')
else:
update_progress(**progress_kwargs,
step=0.9)
text_widget.write('Done!\n')
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
sources[[0,1]] = sources[[1,0]]
return sources
def demucs_prediction(m):
global demucs_sources
mix, samplerate = librosa.load(m, mono=False, sr=44100)
if mix.ndim == 1:
mix = np.asfortranarray([mix,mix])
mix = mix.T
demucs_sources = demix_demucs(mix.T)
if data['demucsmodelVR']:
demucs = HDemucs(sources=["other", "vocals"])
path_d = Path('models/Demucs_Models/v3_repo')
#print('What Demucs model was chosen? ', demucs_model_set)
demucs = _gm(name=demucs_model_set, repo=path_d)
if data['segment'] == 'None':
segment = None
if isinstance(demucs, BagOfModels):
if segment is not None:
for sub in demucs.models:
sub.segment = segment
else:
if segment is not None:
sub.segment = segment
else:
try:
segment = int(data['segment'])
if isinstance(demucs, BagOfModels):
if segment is not None:
for sub in demucs.models:
sub.segment = segment
else:
if segment is not None:
sub.segment = segment
#text_widget.write(base_text + "Segments set to "f"{segment}.\n")
except:
segment = None
if isinstance(demucs, BagOfModels):
if segment is not None:
for sub in demucs.models:
sub.segment = segment
else:
if segment is not None:
sub.segment = segment
demucs.cpu()
demucs.eval()
demucs_prediction(m)
if data['voc_only'] and not data['inst_only']:
pass
else:
text_widget.write(base_text + 'Saving Instrumental... ')
if data['high_end_process'].startswith('mirroring'):
input_high_end_ = spec_utils.mirroring(data['high_end_process'], y_spec_m, input_high_end, mp)
if data['demucsmodelVR']:
wav_instrument = spec_utils.cmb_spectrogram_to_wave_d(y_spec_m, mp, input_high_end_h, input_high_end_, demucs=True)
demucs_inst = demucs_sources[0]
sources = [wav_instrument,demucs_inst]
spec = [stft(sources[0],2048,1024),stft(sources[1],2048,1024)]
ln = min([spec[0].shape[2], spec[1].shape[2]])
spec[0] = spec[0][:,:,:ln]
spec[1] = spec[1][:,:,:ln]
v_spec_c = np.where(np.abs(spec[1]) <= np.abs(spec[0]), spec[1], spec[0])
wav_instrument = istft(v_spec_c,1024)
else:
wav_instrument = spec_utils.cmb_spectrogram_to_wave_d(y_spec_m, mp, input_high_end_h, input_high_end_, demucs=False)
if data['voc_only'] and not data['inst_only']:
pass
else:
text_widget.write('Done!\n')
else:
wav_instrument = spec_utils.cmb_spectrogram_to_wave_d(y_spec_m, mp)
if data['voc_only'] and not data['inst_only']:
pass
else:
text_widget.write('Done!\n')
if data['inst_only'] and not data['voc_only']:
pass
else:
text_widget.write(base_text + 'Saving Vocals... ')
if data['high_end_process'].startswith('mirroring'):
input_high_end_ = spec_utils.mirroring(data['high_end_process'], v_spec_m, input_high_end, mp)
if data['demucsmodelVR']:
wav_vocals = spec_utils.cmb_spectrogram_to_wave_d(v_spec_m, mp, input_high_end_h, input_high_end_, demucs=True)
demucs_voc = demucs_sources[1]
sources = [wav_vocals,demucs_voc]
spec = [stft(sources[0],2048,1024),stft(sources[1],2048,1024)]
ln = min([spec[0].shape[2], spec[1].shape[2]])
spec[0] = spec[0][:,:,:ln]
spec[1] = spec[1][:,:,:ln]
v_spec_c = np.where(np.abs(spec[1]) >= np.abs(spec[0]), spec[1], spec[0])
wav_vocals = istft(v_spec_c,1024)
else:
wav_vocals = spec_utils.cmb_spectrogram_to_wave_d(v_spec_m, mp, input_high_end_h, input_high_end_, demucs=False)
if data['inst_only'] and not data['voc_only']:
pass
else:
text_widget.write('Done!\n')
else:
wav_vocals = spec_utils.cmb_spectrogram_to_wave_d(v_spec_m, mp, demucs=False)
if data['inst_only'] and not data['voc_only']:
pass
else:
text_widget.write('Done!\n')
update_progress(**progress_kwargs,
step=1)
# Save output music files
save_files(wav_instrument, wav_vocals)
update_progress(**progress_kwargs,
step=1)
# Save output image
if data['output_image']:
with open('{}_Instruments.jpg'.format(base_name), mode='wb') as f:
image = spec_utils.spectrogram_to_image(y_spec_m)
_, bin_image = cv2.imencode('.jpg', image)
bin_image.tofile(f)
with open('{}_Vocals.jpg'.format(base_name), mode='wb') as f:
image = spec_utils.spectrogram_to_image(v_spec_m)
_, bin_image = cv2.imencode('.jpg', image)
bin_image.tofile(f)
text_widget.write(base_text + 'Completed Separation!\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