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UVR.py
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# GUI modules
import time
#start_time = time.time()
import audioread
import gui_data.sv_ttk
import hashlib
import json
import librosa
import math
import natsort
import os
import pickle
import psutil
from pyglet import font as pfont
import pyperclip
import base64
import queue
import shutil
import subprocess
import soundfile as sf
import torch
import urllib.request
import webbrowser
import wget
import traceback
import matchering as match
import tkinter as tk
import tkinter.ttk as ttk
from tkinter.font import Font
from tkinter import filedialog
from tkinter import messagebox
from collections import Counter
from __version__ import VERSION, PATCH, PATCH_MAC, PATCH_LINUX
from cryptography.fernet import Fernet
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
from datetime import datetime
from gui_data.constants import *
from gui_data.app_size_values import *
from gui_data.error_handling import error_text, error_dialouge
from gui_data.old_data_check import file_check, remove_unneeded_yamls, remove_temps
from gui_data.tkinterdnd2 import TkinterDnD, DND_FILES
from lib_v5.vr_network.model_param_init import ModelParameters
from kthread import KThread
from lib_v5 import spec_utils
from pathlib import Path
from separate import SeperateDemucs, SeperateMDX, SeperateMDXC, SeperateVR, save_format
from playsound import playsound
from typing import List
import onnx
import re
import sys
import yaml
from ml_collections import ConfigDict
from collections import Counter
# Change the current working directory to the directory
# this file sits in
if getattr(sys, 'frozen', False):
# If the application is run as a bundle, the PyInstaller bootloader
# extends the sys module by a flag frozen=True and sets the app
# path into variable _MEIPASS'.
BASE_PATH = sys._MEIPASS
else:
BASE_PATH = os.path.dirname(os.path.abspath(__file__))
os.chdir(BASE_PATH) # Change the current working directory to the base path
SPLASH_DOC = os.path.join(BASE_PATH, 'tmp', 'splash.txt')
if os.path.isfile(SPLASH_DOC):
os.remove(SPLASH_DOC)
def get_execution_time(function, name):
start = time.time()
function()
end = time.time()
time_difference = end - start
print(f'{name} Execution Time: ', time_difference)
PREVIOUS_PATCH_WIN = 'UVR_Patch_3_31_23_5_5'
is_dnd_compatible = True
banner_placement = -2
if OPERATING_SYSTEM=="Darwin":
OPEN_FILE_func = lambda input_string:subprocess.Popen(["open", input_string])
dnd_path_check = MAC_DND_CHECK
banner_placement = -8
current_patch = PATCH_MAC
is_windows = False
is_macos = True
right_click_button = '<Button-2>'
application_extension = ".dmg"
elif OPERATING_SYSTEM=="Linux":
OPEN_FILE_func = lambda input_string:subprocess.Popen(["open", input_string])
dnd_path_check = LINUX_DND_CHECK
current_patch = PATCH_LINUX
is_windows = False
is_macos = False
right_click_button = '<Button-3>'
application_extension = ".zip"
elif OPERATING_SYSTEM=="Windows":
OPEN_FILE_func = lambda input_string:os.startfile(input_string)
dnd_path_check = WINDOWS_DND_CHECK
current_patch = PATCH
is_windows = True
is_macos = False
right_click_button = '<Button-3>'
application_extension = ".exe"
def right_click_release_linux(window, top_win=None):
if OPERATING_SYSTEM=="Linux":
root.bind('<Button-1>', lambda e:window.destroy())
if top_win:
top_win.bind('<Button-1>', lambda e:window.destroy())
if not is_windows:
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
else:
from ctypes import windll, wintypes
def close_process(q:queue.Queue):
def close_splash():
name = "UVR_Launcher.exe"
for process in psutil.process_iter(attrs=["name"]):
process_name = process.info.get("name")
if process_name == name:
try:
process.terminate()
q.put(f"{name} terminated.") # Push message to queue
break
except psutil.NoSuchProcess as e:
q.put(f"Error terminating {name}: {e}") # Push error to queue
try:
with open(SPLASH_DOC, 'w') as f:
f.write('1')
except:
print('No splash screen.')
thread = KThread(target=close_splash)
thread.start()
def save_data(data):
"""
Saves given data as a .pkl (pickle) file
Paramters:
data(dict):
Dictionary containing all the necessary data to save
"""
# Open data file, create it if it does not exist
with open('data.pkl', 'wb') as data_file:
pickle.dump(data, data_file)
def load_data() -> dict:
"""
Loads saved pkl file and returns the stored data
Returns(dict):
Dictionary containing all the saved data
"""
try:
with open('data.pkl', 'rb') as data_file: # Open data file
data = pickle.load(data_file)
return data
except (ValueError, FileNotFoundError):
# Data File is corrupted or not found so recreate it
save_data(data=DEFAULT_DATA)
return load_data()
def load_model_hash_data(dictionary):
'''Get the model hash dictionary'''
with open(dictionary, 'r') as d:
return json.load(d)
def font_checker(font_file):
chosen_font_name = None
chosen_font_file = None
try:
if os.path.isfile(font_file):
with open(font_file, 'r') as d:
chosen_font = json.load(d)
chosen_font_name = chosen_font["font_name"]
if chosen_font["font_file"]:
chosen_font_file = os.path.join(OTHER_FONT_PATH, chosen_font["font_file"])
chosen_font_file = chosen_font_file if os.path.isfile(chosen_font_file) else None
except Exception as e:
print(e)
chosen_font = chosen_font_name, chosen_font_file
return chosen_font
debugger = []
#--Constants--
#Models
MODELS_DIR = os.path.join(BASE_PATH, 'models')
VR_MODELS_DIR = os.path.join(MODELS_DIR, 'VR_Models')
MDX_MODELS_DIR = os.path.join(MODELS_DIR, 'MDX_Net_Models')
DEMUCS_MODELS_DIR = os.path.join(MODELS_DIR, 'Demucs_Models')
DEMUCS_NEWER_REPO_DIR = os.path.join(DEMUCS_MODELS_DIR, 'v3_v4_repo')
MDX_MIXER_PATH = os.path.join(BASE_PATH, 'lib_v5', 'mixer.ckpt')
#Cache & Parameters
VR_HASH_DIR = os.path.join(VR_MODELS_DIR, 'model_data')
VR_HASH_JSON = os.path.join(VR_MODELS_DIR, 'model_data', 'model_data.json')
MDX_HASH_DIR = os.path.join(MDX_MODELS_DIR, 'model_data')
MDX_HASH_JSON = os.path.join(MDX_HASH_DIR, 'model_data.json')
MDX_C_CONFIG_PATH = os.path.join(MDX_HASH_DIR, 'mdx_c_configs')
DEMUCS_MODEL_NAME_SELECT = os.path.join(DEMUCS_MODELS_DIR, 'model_data', 'model_name_mapper.json')
MDX_MODEL_NAME_SELECT = os.path.join(MDX_MODELS_DIR, 'model_data', 'model_name_mapper.json')
ENSEMBLE_CACHE_DIR = os.path.join(BASE_PATH, 'gui_data', 'saved_ensembles')
SETTINGS_CACHE_DIR = os.path.join(BASE_PATH, 'gui_data', 'saved_settings')
VR_PARAM_DIR = os.path.join(BASE_PATH, 'lib_v5', 'vr_network', 'modelparams')
SAMPLE_CLIP_PATH = os.path.join(BASE_PATH, 'temp_sample_clips')
ENSEMBLE_TEMP_PATH = os.path.join(BASE_PATH, 'ensemble_temps')
DOWNLOAD_MODEL_CACHE = os.path.join(BASE_PATH, 'gui_data', 'model_manual_download.json')
#CR Text
CR_TEXT = os.path.join(BASE_PATH, 'gui_data', 'cr_text.txt')
#Style
ICON_IMG_PATH = os.path.join(BASE_PATH, 'gui_data', 'img', 'GUI-Icon.ico')
if not is_windows:
MAIN_ICON_IMG_PATH = os.path.join(BASE_PATH, 'gui_data', 'img', 'GUI-Icon.png')
OWN_FONT_PATH = os.path.join(BASE_PATH, 'gui_data', 'own_font.json')
MAIN_FONT_NAME = 'Montserrat'
SEC_FONT_NAME = 'Century Gothic'
FONT_PATH = os.path.join(BASE_PATH, 'gui_data', 'fonts', 'Montserrat', 'Montserrat.ttf')#
SEC_FONT_PATH = os.path.join(BASE_PATH, 'gui_data', 'fonts', 'centurygothic', 'GOTHIC.ttf')#
OTHER_FONT_PATH = os.path.join(BASE_PATH, 'gui_data', 'fonts', 'other')#
FONT_MAPPER = {MAIN_FONT_NAME:FONT_PATH,
SEC_FONT_NAME:SEC_FONT_PATH}
#Other
COMPLETE_CHIME = os.path.join(BASE_PATH, 'gui_data', 'complete_chime.wav')
FAIL_CHIME = os.path.join(BASE_PATH, 'gui_data', 'fail_chime.wav')
CHANGE_LOG = os.path.join(BASE_PATH, 'gui_data', 'change_log.txt')
DENOISER_MODEL_PATH = os.path.join(VR_MODELS_DIR, 'UVR-DeNoise-Lite.pth')
DEVERBER_MODEL_PATH = os.path.join(VR_MODELS_DIR, 'UVR-DeEcho-DeReverb.pth')
MODEL_DATA_URLS = [VR_MODEL_DATA_LINK, MDX_MODEL_DATA_LINK, MDX_MODEL_NAME_DATA_LINK, DEMUCS_MODEL_NAME_DATA_LINK]
MODEL_DATA_FILES = [VR_HASH_JSON, MDX_HASH_JSON, MDX_MODEL_NAME_SELECT, DEMUCS_MODEL_NAME_SELECT]
file_check(os.path.join(MODELS_DIR, 'Main_Models'), VR_MODELS_DIR)
file_check(os.path.join(DEMUCS_MODELS_DIR, 'v3_repo'), DEMUCS_NEWER_REPO_DIR)
remove_unneeded_yamls(DEMUCS_MODELS_DIR)
remove_temps(ENSEMBLE_TEMP_PATH)
remove_temps(SAMPLE_CLIP_PATH)
remove_temps(os.path.join(BASE_PATH, 'img'))
if not os.path.isdir(ENSEMBLE_TEMP_PATH):
os.mkdir(ENSEMBLE_TEMP_PATH)
if not os.path.isdir(SAMPLE_CLIP_PATH):
os.mkdir(SAMPLE_CLIP_PATH)
model_hash_table = {}
data = load_data()
def drop(event, accept_mode: str = 'files'):
path = event.data
if accept_mode == 'folder':
path = path.replace('{', '').replace('}', '')
if not os.path.isdir(path):
messagebox.showerror(parent=root,
title=INVALID_FOLDER_ERROR_TEXT[0],
message=INVALID_FOLDER_ERROR_TEXT[1])
return
root.export_path_var.set(path)
elif accept_mode in ['files', FILE_1, FILE_2, FILE_1_LB, FILE_2_LB]:
path = path.replace("{", "").replace("}", "")
for dnd_file in dnd_path_check:
path = path.replace(f" {dnd_file}", f";{dnd_file}")
path = path.split(';')
path[-1] = path[-1].replace(';', '')
if accept_mode == 'files':
root.inputPaths = tuple(path)
root.process_input_selections()
root.update_inputPaths()
elif accept_mode in [FILE_1, FILE_2]:
if len(path) == 2:
root.select_audiofile(path[0])
root.select_audiofile(path[1], is_primary=False)
root.DualBatch_inputPaths = []
root.check_dual_paths()
elif len(path) == 1:
if accept_mode == FILE_1:
root.select_audiofile(path[0])
else:
root.select_audiofile(path[0], is_primary=False)
elif accept_mode in [FILE_1_LB, FILE_2_LB]:
return path
else:
return
class ModelData():
def __init__(self, model_name: str,
selected_process_method=ENSEMBLE_MODE,
is_secondary_model=False,
primary_model_primary_stem=None,
is_primary_model_primary_stem_only=False,
is_primary_model_secondary_stem_only=False,
is_pre_proc_model=False,
is_dry_check=False,
is_change_def=False,
is_get_hash_dir_only=False,
is_vocal_split_model=False):
self.DENOISER_MODEL = DENOISER_MODEL_PATH
self.DEVERBER_MODEL = DEVERBER_MODEL_PATH
self.is_deverb_vocals = root.is_deverb_vocals_var.get() if os.path.isfile(DEVERBER_MODEL_PATH) else False
self.deverb_vocal_opt = DEVERB_MAPPER[root.deverb_vocal_opt_var.get()]
self.is_denoise_model = True if root.denoise_option_var.get() == DENOISE_M and os.path.isfile(DENOISER_MODEL_PATH) else False
self.is_gpu_conversion = 0 if root.is_gpu_conversion_var.get() else -1
self.is_normalization = root.is_normalization_var.get()
self.is_primary_stem_only = root.is_primary_stem_only_var.get()
self.is_secondary_stem_only = root.is_secondary_stem_only_var.get()
self.is_denoise = True if not root.denoise_option_var.get() == DENOISE_NONE else False
self.is_mdx_c_seg_def = root.is_mdx_c_seg_def_var.get()#
self.mdx_batch_size = 1 if root.mdx_batch_size_var.get() == DEF_OPT else int(root.mdx_batch_size_var.get())
self.mdxnet_stem_select = root.mdxnet_stems_var.get()
self.overlap = float(root.overlap_var.get()) if not root.overlap_var.get() == DEFAULT else 0.25
self.overlap_mdx = float(root.overlap_mdx_var.get()) if not root.overlap_mdx_var.get() == DEFAULT else root.overlap_mdx_var.get()
self.overlap_mdx23 = int(float(root.overlap_mdx23_var.get()))
self.semitone_shift = float(root.semitone_shift_var.get())
self.is_pitch_change = False if self.semitone_shift == 0 else True
self.is_match_frequency_pitch = root.is_match_frequency_pitch_var.get()
self.is_mdx_ckpt = False
self.is_mdx_c = False
self.is_mdx_combine_stems = root.is_mdx23_combine_stems_var.get()#
self.mdx_c_configs = None
self.mdx_model_stems = []
self.mdx_dim_f_set = None
self.mdx_dim_t_set = None
self.mdx_stem_count = 1
self.compensate = None
self.mdx_n_fft_scale_set = None
self.wav_type_set = root.wav_type_set
self.mp3_bit_set = root.mp3_bit_set_var.get()
self.save_format = root.save_format_var.get()
self.is_invert_spec = root.is_invert_spec_var.get()#
self.is_mixer_mode = False#
self.demucs_stems = root.demucs_stems_var.get()
self.is_demucs_combine_stems = root.is_demucs_combine_stems_var.get()
self.demucs_source_list = []
self.demucs_stem_count = 0
self.mixer_path = MDX_MIXER_PATH
self.model_name = model_name
self.process_method = selected_process_method
self.model_status = False if self.model_name == CHOOSE_MODEL or self.model_name == NO_MODEL else True
self.primary_stem = None
self.secondary_stem = None
self.primary_stem_native = None
self.is_ensemble_mode = False
self.ensemble_primary_stem = None
self.ensemble_secondary_stem = None
self.primary_model_primary_stem = primary_model_primary_stem
self.is_secondary_model = True if is_vocal_split_model else is_secondary_model
self.secondary_model = None
self.secondary_model_scale = None
self.demucs_4_stem_added_count = 0
self.is_demucs_4_stem_secondaries = False
self.is_4_stem_ensemble = False
self.pre_proc_model = None
self.pre_proc_model_activated = False
self.is_pre_proc_model = is_pre_proc_model
self.is_dry_check = is_dry_check
self.model_samplerate = 44100
self.model_capacity = 32, 128
self.is_vr_51_model = False
self.is_demucs_pre_proc_model_inst_mix = False
self.manual_download_Button = None
self.secondary_model_4_stem = []
self.secondary_model_4_stem_scale = []
self.secondary_model_4_stem_names = []
self.secondary_model_4_stem_model_names_list = []
self.all_models = []
self.secondary_model_other = None
self.secondary_model_scale_other = None
self.secondary_model_bass = None
self.secondary_model_scale_bass = None
self.secondary_model_drums = None
self.secondary_model_scale_drums = None
self.is_multi_stem_ensemble = False
self.is_karaoke = False
self.is_bv_model = False
self.bv_model_rebalance = 0
self.is_sec_bv_rebalance = False
self.is_change_def = is_change_def
self.model_hash_dir = None
self.is_get_hash_dir_only = is_get_hash_dir_only
self.is_secondary_model_activated = False
self.vocal_split_model = None
self.is_vocal_split_model = is_vocal_split_model
self.is_vocal_split_model_activated = False
self.is_save_inst_vocal_splitter = root.is_save_inst_set_vocal_splitter_var.get()
self.is_inst_only_voc_splitter = root.check_only_selection_stem(INST_STEM_ONLY)
self.is_save_vocal_only = root.check_only_selection_stem(IS_SAVE_VOC_ONLY)
if selected_process_method == ENSEMBLE_MODE:
self.process_method, _, self.model_name = model_name.partition(ENSEMBLE_PARTITION)
self.model_and_process_tag = model_name
self.ensemble_primary_stem, self.ensemble_secondary_stem = root.return_ensemble_stems()
is_not_secondary_or_pre_proc = not is_secondary_model and not is_pre_proc_model
self.is_ensemble_mode = is_not_secondary_or_pre_proc
if root.ensemble_main_stem_var.get() == FOUR_STEM_ENSEMBLE:
self.is_4_stem_ensemble = self.is_ensemble_mode
elif root.ensemble_main_stem_var.get() == MULTI_STEM_ENSEMBLE and root.chosen_process_method_var.get() == ENSEMBLE_MODE:
self.is_multi_stem_ensemble = True
is_not_vocal_stem = self.ensemble_primary_stem != VOCAL_STEM
self.pre_proc_model_activated = root.is_demucs_pre_proc_model_activate_var.get() if is_not_vocal_stem else False
if self.process_method == VR_ARCH_TYPE:
self.is_secondary_model_activated = root.vr_is_secondary_model_activate_var.get() if not is_secondary_model else False
self.aggression_setting = float(int(root.aggression_setting_var.get())/100)
self.is_tta = root.is_tta_var.get()
self.is_post_process = root.is_post_process_var.get()
self.window_size = int(root.window_size_var.get())
self.batch_size = 1 if root.batch_size_var.get() == DEF_OPT else int(root.batch_size_var.get())
self.crop_size = int(root.crop_size_var.get())
self.is_high_end_process = 'mirroring' if root.is_high_end_process_var.get() else 'None'
self.post_process_threshold = float(root.post_process_threshold_var.get())
self.model_capacity = 32, 128
self.model_path = os.path.join(VR_MODELS_DIR, f"{self.model_name}.pth")
self.get_model_hash()
if self.model_hash:
self.model_hash_dir = os.path.join(VR_HASH_DIR, f"{self.model_hash}.json")
if is_change_def:
self.model_data = self.change_model_data()
else:
self.model_data = self.get_model_data(VR_HASH_DIR, root.vr_hash_MAPPER) if not self.model_hash == WOOD_INST_MODEL_HASH else WOOD_INST_PARAMS
if self.model_data:
vr_model_param = os.path.join(VR_PARAM_DIR, "{}.json".format(self.model_data["vr_model_param"]))
self.primary_stem = self.model_data["primary_stem"]
self.secondary_stem = secondary_stem(self.primary_stem)
self.vr_model_param = ModelParameters(vr_model_param)
self.model_samplerate = self.vr_model_param.param['sr']
self.primary_stem_native = self.primary_stem
if "nout" in self.model_data.keys() and "nout_lstm" in self.model_data.keys():
self.model_capacity = self.model_data["nout"], self.model_data["nout_lstm"]
self.is_vr_51_model = True
self.check_if_karaokee_model()
else:
self.model_status = False
if self.process_method == MDX_ARCH_TYPE:
self.is_secondary_model_activated = root.mdx_is_secondary_model_activate_var.get() if not is_secondary_model else False
self.margin = int(root.margin_var.get())
self.chunks = 0
self.mdx_segment_size = int(root.mdx_segment_size_var.get())
self.get_mdx_model_path()
self.get_model_hash()
if self.model_hash:
self.model_hash_dir = os.path.join(MDX_HASH_DIR, f"{self.model_hash}.json")
if is_change_def:
self.model_data = self.change_model_data()
else:
self.model_data = self.get_model_data(MDX_HASH_DIR, root.mdx_hash_MAPPER)
if self.model_data:
if "config_yaml" in self.model_data:
self.is_mdx_c = True
config_path = os.path.join(MDX_C_CONFIG_PATH, self.model_data["config_yaml"])
if os.path.isfile(config_path):
with open(config_path) as f:
config = ConfigDict(yaml.load(f, Loader=yaml.FullLoader))
self.mdx_c_configs = config
if self.mdx_c_configs.training.target_instrument:
# Use target_instrument as the primary stem and set 4-stem ensemble to False
target = self.mdx_c_configs.training.target_instrument
self.mdx_model_stems = [target]
self.primary_stem = target
else:
# If no specific target_instrument, use all instruments in the training config
self.mdx_model_stems = self.mdx_c_configs.training.instruments
self.mdx_stem_count = len(self.mdx_model_stems)
# Set primary stem based on stem count
if self.mdx_stem_count == 2:
self.primary_stem = self.mdx_model_stems[0]
else:
self.primary_stem = self.mdxnet_stem_select
# Update mdxnet_stem_select based on ensemble mode
if self.is_ensemble_mode:
self.mdxnet_stem_select = self.ensemble_primary_stem
else:
self.model_status = False
else:
self.compensate = self.model_data["compensate"] if root.compensate_var.get() == AUTO_SELECT else float(root.compensate_var.get())
self.mdx_dim_f_set = self.model_data["mdx_dim_f_set"]
self.mdx_dim_t_set = self.model_data["mdx_dim_t_set"]
self.mdx_n_fft_scale_set = self.model_data["mdx_n_fft_scale_set"]
self.primary_stem = self.model_data["primary_stem"]
self.primary_stem_native = self.model_data["primary_stem"]
self.check_if_karaokee_model()
self.secondary_stem = secondary_stem(self.primary_stem)
else:
self.model_status = False
if self.process_method == DEMUCS_ARCH_TYPE:
self.is_secondary_model_activated = root.demucs_is_secondary_model_activate_var.get() if not is_secondary_model else False
if not self.is_ensemble_mode:
self.pre_proc_model_activated = root.is_demucs_pre_proc_model_activate_var.get() if not root.demucs_stems_var.get() in [VOCAL_STEM, INST_STEM] else False
self.margin_demucs = int(root.margin_demucs_var.get())
self.chunks_demucs = 0
self.shifts = int(root.shifts_var.get())
self.is_split_mode = root.is_split_mode_var.get()
self.segment = root.segment_var.get()
self.is_chunk_demucs = root.is_chunk_demucs_var.get()
self.is_primary_stem_only = root.is_primary_stem_only_var.get() if self.is_ensemble_mode else root.is_primary_stem_only_Demucs_var.get()
self.is_secondary_stem_only = root.is_secondary_stem_only_var.get() if self.is_ensemble_mode else root.is_secondary_stem_only_Demucs_var.get()
self.get_demucs_model_data()
self.get_demucs_model_path()
if self.model_status:
self.model_basename = os.path.splitext(os.path.basename(self.model_path))[0]
else:
self.model_basename = None
self.pre_proc_model_activated = self.pre_proc_model_activated if not self.is_secondary_model else False
self.is_primary_model_primary_stem_only = is_primary_model_primary_stem_only
self.is_primary_model_secondary_stem_only = is_primary_model_secondary_stem_only
is_secondary_activated_and_status = self.is_secondary_model_activated and self.model_status
is_demucs = self.process_method == DEMUCS_ARCH_TYPE
is_all_stems = root.demucs_stems_var.get() == ALL_STEMS
is_valid_ensemble = not self.is_ensemble_mode and is_all_stems and is_demucs
is_multi_stem_ensemble_demucs = self.is_multi_stem_ensemble and is_demucs
if is_secondary_activated_and_status:
if is_valid_ensemble or self.is_4_stem_ensemble or is_multi_stem_ensemble_demucs:
for key in DEMUCS_4_SOURCE_LIST:
self.secondary_model_data(key)
self.secondary_model_4_stem.append(self.secondary_model)
self.secondary_model_4_stem_scale.append(self.secondary_model_scale)
self.secondary_model_4_stem_names.append(key)
self.demucs_4_stem_added_count = sum(i is not None for i in self.secondary_model_4_stem)
self.is_secondary_model_activated = any(i is not None for i in self.secondary_model_4_stem)
self.demucs_4_stem_added_count -= 1 if self.is_secondary_model_activated else 0
if self.is_secondary_model_activated:
self.secondary_model_4_stem_model_names_list = [i.model_basename if i is not None else None for i in self.secondary_model_4_stem]
self.is_demucs_4_stem_secondaries = True
else:
primary_stem = self.ensemble_primary_stem if self.is_ensemble_mode and is_demucs else self.primary_stem
self.secondary_model_data(primary_stem)
if self.process_method == DEMUCS_ARCH_TYPE and not is_secondary_model:
if self.demucs_stem_count >= 3 and self.pre_proc_model_activated:
self.pre_proc_model = root.process_determine_demucs_pre_proc_model(self.primary_stem)
self.pre_proc_model_activated = True if self.pre_proc_model else False
self.is_demucs_pre_proc_model_inst_mix = root.is_demucs_pre_proc_model_inst_mix_var.get() if self.pre_proc_model else False
if self.is_vocal_split_model and self.model_status:
self.is_secondary_model_activated = False
if self.is_bv_model:
primary = BV_VOCAL_STEM if self.primary_stem_native == VOCAL_STEM else LEAD_VOCAL_STEM
else:
primary = LEAD_VOCAL_STEM if self.primary_stem_native == VOCAL_STEM else BV_VOCAL_STEM
self.primary_stem, self.secondary_stem = primary, secondary_stem(primary)
self.vocal_splitter_model_data()
def vocal_splitter_model_data(self):
if not self.is_secondary_model and self.model_status:
self.vocal_split_model = root.process_determine_vocal_split_model()
self.is_vocal_split_model_activated = True if self.vocal_split_model else False
if self.vocal_split_model:
if self.vocal_split_model.bv_model_rebalance:
self.is_sec_bv_rebalance = True
def secondary_model_data(self, primary_stem):
secondary_model_data = root.process_determine_secondary_model(self.process_method, primary_stem, self.is_primary_stem_only, self.is_secondary_stem_only)
self.secondary_model = secondary_model_data[0]
self.secondary_model_scale = secondary_model_data[1]
self.is_secondary_model_activated = False if not self.secondary_model else True
if self.secondary_model:
self.is_secondary_model_activated = False if self.secondary_model.model_basename == self.model_basename else True
#print("self.is_secondary_model_activated: ", self.is_secondary_model_activated)
def check_if_karaokee_model(self):
if IS_KARAOKEE in self.model_data.keys():
self.is_karaoke = self.model_data[IS_KARAOKEE]
if IS_BV_MODEL in self.model_data.keys():
self.is_bv_model = self.model_data[IS_BV_MODEL]#
if IS_BV_MODEL_REBAL in self.model_data.keys() and self.is_bv_model:
self.bv_model_rebalance = self.model_data[IS_BV_MODEL_REBAL]#
def get_mdx_model_path(self):
if self.model_name.endswith(CKPT):
self.is_mdx_ckpt = True
ext = '' if self.is_mdx_ckpt else ONNX
for file_name, chosen_mdx_model in root.mdx_name_select_MAPPER.items():
if self.model_name in chosen_mdx_model:
if file_name.endswith(CKPT):
ext = ''
self.model_path = os.path.join(MDX_MODELS_DIR, f"{file_name}{ext}")
break
else:
self.model_path = os.path.join(MDX_MODELS_DIR, f"{self.model_name}{ext}")
self.mixer_path = os.path.join(MDX_MODELS_DIR, f"mixer_val.ckpt")
def get_demucs_model_path(self):
demucs_newer = self.demucs_version in {DEMUCS_V3, DEMUCS_V4}
demucs_model_dir = DEMUCS_NEWER_REPO_DIR if demucs_newer else DEMUCS_MODELS_DIR
for file_name, chosen_model in root.demucs_name_select_MAPPER.items():
if self.model_name == chosen_model:
self.model_path = os.path.join(demucs_model_dir, file_name)
break
else:
self.model_path = os.path.join(DEMUCS_NEWER_REPO_DIR, f'{self.model_name}.yaml')
def get_demucs_model_data(self):
self.demucs_version = DEMUCS_V4
for key, value in DEMUCS_VERSION_MAPPER.items():
if value in self.model_name:
self.demucs_version = key
if DEMUCS_UVR_MODEL in self.model_name:
self.demucs_source_list, self.demucs_source_map, self.demucs_stem_count = DEMUCS_2_SOURCE, DEMUCS_2_SOURCE_MAPPER, 2
else:
self.demucs_source_list, self.demucs_source_map, self.demucs_stem_count = DEMUCS_4_SOURCE, DEMUCS_4_SOURCE_MAPPER, 4
if not self.is_ensemble_mode:
self.primary_stem = PRIMARY_STEM if self.demucs_stems == ALL_STEMS else self.demucs_stems
self.secondary_stem = secondary_stem(self.primary_stem)
def get_model_data(self, model_hash_dir, hash_mapper:dict):
model_settings_json = os.path.join(model_hash_dir, f"{self.model_hash}.json")
if os.path.isfile(model_settings_json):
with open(model_settings_json, 'r') as json_file:
return json.load(json_file)
else:
for hash, settings in hash_mapper.items():
if self.model_hash in hash:
return settings
return self.get_model_data_from_popup()
def change_model_data(self):
if self.is_get_hash_dir_only:
return None
else:
return self.get_model_data_from_popup()
def get_model_data_from_popup(self):
if self.is_dry_check:
return None
if not self.is_change_def:
confirm = messagebox.askyesno(
title=UNRECOGNIZED_MODEL[0],
message=f'"{self.model_name}"{UNRECOGNIZED_MODEL[1]}',
parent=root
)
if not confirm:
return None
if self.process_method == VR_ARCH_TYPE:
root.pop_up_vr_param(self.model_hash)
return root.vr_model_params
elif self.process_method == MDX_ARCH_TYPE:
root.pop_up_mdx_model(self.model_hash, self.model_path)
return root.mdx_model_params
def get_model_hash(self):
self.model_hash = None
if not os.path.isfile(self.model_path):
self.model_status = False
self.model_hash is None
else:
if model_hash_table:
for (key, value) in model_hash_table.items():
if self.model_path == key:
self.model_hash = value
break
if not self.model_hash:
try:
with open(self.model_path, 'rb') as f:
f.seek(- 10000 * 1024, 2)
self.model_hash = hashlib.md5(f.read()).hexdigest()
except:
self.model_hash = hashlib.md5(open(self.model_path,'rb').read()).hexdigest()
table_entry = {self.model_path: self.model_hash}
model_hash_table.update(table_entry)
#print(self.model_name," - ", self.model_hash)
class Ensembler():
def __init__(self, is_manual_ensemble=False):
self.is_save_all_outputs_ensemble = root.is_save_all_outputs_ensemble_var.get()
chosen_ensemble_name = '{}'.format(root.chosen_ensemble_var.get().replace(" ", "_")) if not root.chosen_ensemble_var.get() == CHOOSE_ENSEMBLE_OPTION else 'Ensembled'
ensemble_algorithm = root.ensemble_type_var.get().partition("/")
ensemble_main_stem_pair = root.ensemble_main_stem_var.get().partition("/")
time_stamp = round(time.time())
self.audio_tool = MANUAL_ENSEMBLE
self.main_export_path = Path(root.export_path_var.get())
self.chosen_ensemble = f"_{chosen_ensemble_name}" if root.is_append_ensemble_name_var.get() else ''
ensemble_folder_name = self.main_export_path if self.is_save_all_outputs_ensemble else ENSEMBLE_TEMP_PATH
self.ensemble_folder_name = os.path.join(ensemble_folder_name, '{}_Outputs_{}'.format(chosen_ensemble_name, time_stamp))
self.is_testing_audio = f"{time_stamp}_" if root.is_testing_audio_var.get() else ''
self.primary_algorithm = ensemble_algorithm[0]
self.secondary_algorithm = ensemble_algorithm[2]
self.ensemble_primary_stem = ensemble_main_stem_pair[0]
self.ensemble_secondary_stem = ensemble_main_stem_pair[2]
self.is_normalization = root.is_normalization_var.get()
self.is_wav_ensemble = root.is_wav_ensemble_var.get()
self.wav_type_set = root.wav_type_set
self.mp3_bit_set = root.mp3_bit_set_var.get()
self.save_format = root.save_format_var.get()
if not is_manual_ensemble:
os.mkdir(self.ensemble_folder_name)
def ensemble_outputs(self, audio_file_base, export_path, stem, is_4_stem=False, is_inst_mix=False):
"""Processes the given outputs and ensembles them with the chosen algorithm"""
if is_4_stem:
algorithm = root.ensemble_type_var.get()
stem_tag = stem
else:
if is_inst_mix:
algorithm = self.secondary_algorithm
stem_tag = f"{self.ensemble_secondary_stem} {INST_STEM}"
else:
algorithm = self.primary_algorithm if stem == PRIMARY_STEM else self.secondary_algorithm
stem_tag = self.ensemble_primary_stem if stem == PRIMARY_STEM else self.ensemble_secondary_stem
stem_outputs = self.get_files_to_ensemble(folder=export_path, prefix=audio_file_base, suffix=f"_({stem_tag}).wav")
audio_file_output = f"{self.is_testing_audio}{audio_file_base}{self.chosen_ensemble}_({stem_tag})"
stem_save_path = os.path.join('{}'.format(self.main_export_path),'{}.wav'.format(audio_file_output))
#print("get_files_to_ensemble: ", stem_outputs)
if len(stem_outputs) > 1:
spec_utils.ensemble_inputs(stem_outputs, algorithm, self.is_normalization, self.wav_type_set, stem_save_path, is_wave=self.is_wav_ensemble)
save_format(stem_save_path, self.save_format, self.mp3_bit_set)
if self.is_save_all_outputs_ensemble:
for i in stem_outputs:
save_format(i, self.save_format, self.mp3_bit_set)
else:
for i in stem_outputs:
try:
os.remove(i)
except Exception as e:
print(e)
def ensemble_manual(self, audio_inputs, audio_file_base, is_bulk=False):
"""Processes the given outputs and ensembles them with the chosen algorithm"""
is_mv_sep = True
if is_bulk:
number_list = list(set([os.path.basename(i).split("_")[0] for i in audio_inputs]))
for n in number_list:
current_list = [i for i in audio_inputs if os.path.basename(i).startswith(n)]
audio_file_base = os.path.basename(current_list[0]).split('.wav')[0]
stem_testing = "instrum" if "Instrumental" in audio_file_base else "vocals"
if is_mv_sep:
audio_file_base = audio_file_base.split("_")
audio_file_base = f"{audio_file_base[1]}_{audio_file_base[2]}_{stem_testing}"
self.ensemble_manual_process(current_list, audio_file_base, is_bulk)
else:
self.ensemble_manual_process(audio_inputs, audio_file_base, is_bulk)
def ensemble_manual_process(self, audio_inputs, audio_file_base, is_bulk):
algorithm = root.choose_algorithm_var.get()
algorithm_text = "" if is_bulk else f"_({root.choose_algorithm_var.get()})"
stem_save_path = os.path.join('{}'.format(self.main_export_path),'{}{}{}.wav'.format(self.is_testing_audio, audio_file_base, algorithm_text))
spec_utils.ensemble_inputs(audio_inputs, algorithm, self.is_normalization, self.wav_type_set, stem_save_path, is_wave=self.is_wav_ensemble)
save_format(stem_save_path, self.save_format, self.mp3_bit_set)
def get_files_to_ensemble(self, folder="", prefix="", suffix=""):
"""Grab all the files to be ensembled"""
return [os.path.join(folder, i) for i in os.listdir(folder) if i.startswith(prefix) and i.endswith(suffix)]
def combine_audio(self, audio_inputs, audio_file_base):
save_format_ = lambda save_path:save_format(save_path, root.save_format_var.get(), root.mp3_bit_set_var.get())
spec_utils.combine_audio(audio_inputs,
os.path.join(self.main_export_path, f"{self.is_testing_audio}{audio_file_base}"),
self.wav_type_set,
save_format=save_format_)
class AudioTools():
def __init__(self, audio_tool):
time_stamp = round(time.time())
self.audio_tool = audio_tool
self.main_export_path = Path(root.export_path_var.get())
self.wav_type_set = root.wav_type_set
self.is_normalization = root.is_normalization_var.get()
self.is_testing_audio = f"{time_stamp}_" if root.is_testing_audio_var.get() else ''
self.save_format = lambda save_path:save_format(save_path, root.save_format_var.get(), root.mp3_bit_set_var.get())
self.align_window = TIME_WINDOW_MAPPER[root.time_window_var.get()]
self.align_intro_val = INTRO_MAPPER[root.intro_analysis_var.get()]
self.db_analysis_val = VOLUME_MAPPER[root.db_analysis_var.get()]
self.is_save_align = root.is_save_align_var.get()#
self.is_match_silence = root.is_match_silence_var.get()#
self.is_spec_match = root.is_spec_match_var.get()
self.phase_option = root.phase_option_var.get()#
self.phase_shifts = PHASE_SHIFTS_OPT[root.phase_shifts_var.get()]
def align_inputs(self, audio_inputs, audio_file_base, audio_file_2_base, command_Text, set_progress_bar):
audio_file_base = f"{self.is_testing_audio}{audio_file_base}"
audio_file_2_base = f"{self.is_testing_audio}{audio_file_2_base}"
aligned_path = os.path.join('{}'.format(self.main_export_path),'{}_(Aligned).wav'.format(audio_file_2_base))
inverted_path = os.path.join('{}'.format(self.main_export_path),'{}_(Inverted).wav'.format(audio_file_base))
spec_utils.align_audio(audio_inputs[0],
audio_inputs[1],
aligned_path,
inverted_path,
self.wav_type_set,
self.is_save_align,
command_Text,
self.save_format,
align_window=self.align_window,
align_intro_val=self.align_intro_val,
db_analysis=self.db_analysis_val,
set_progress_bar=set_progress_bar,
phase_option=self.phase_option,
phase_shifts=self.phase_shifts,
is_match_silence=self.is_match_silence,
is_spec_match=self.is_spec_match)
def match_inputs(self, audio_inputs, audio_file_base, command_Text):
target = audio_inputs[0]
reference = audio_inputs[1]
command_Text(f"Processing... ")
save_path = os.path.join('{}'.format(self.main_export_path),'{}_(Matched).wav'.format(f"{self.is_testing_audio}{audio_file_base}"))
match.process(
target=target,
reference=reference,
results=[match.save_audiofile(save_path, wav_set=self.wav_type_set),
],
)
self.save_format(save_path)
def combine_audio(self, audio_inputs, audio_file_base):
spec_utils.combine_audio(audio_inputs,
os.path.join(self.main_export_path, f"{self.is_testing_audio}{audio_file_base}"),
self.wav_type_set,
save_format=self.save_format)
def pitch_or_time_shift(self, audio_file, audio_file_base):
is_time_correction = True
rate = float(root.time_stretch_rate_var.get()) if self.audio_tool == TIME_STRETCH else float(root.pitch_rate_var.get())
is_pitch = False if self.audio_tool == TIME_STRETCH else True
if is_pitch:
is_time_correction = True if root.is_time_correction_var.get() else False
file_text = TIME_TEXT if self.audio_tool == TIME_STRETCH else PITCH_TEXT
save_path = os.path.join(self.main_export_path, f"{self.is_testing_audio}{audio_file_base}{file_text}.wav")
spec_utils.augment_audio(save_path, audio_file, rate, self.is_normalization, self.wav_type_set, self.save_format, is_pitch=is_pitch, is_time_correction=is_time_correction)
class ToolTip(object):
def __init__(self, widget):
self.widget = widget
self.tooltip = None
def showtip(self, text, is_message_box=False, is_success_message=None):#
self.hidetip()
def create_label_config():
font_size = FONT_SIZE_3 if is_message_box else FONT_SIZE_2
"""Helper function to generate label configurations."""
common_config = {
"text": text,
"relief": tk.SOLID,
"borderwidth": 1,
"font": (MAIN_FONT_NAME, f"{font_size}", "normal")
}
if is_message_box:
background_color = "#03692d" if is_success_message else "#8B0000"
return {**common_config, "background": background_color, "foreground": "#ffffff"}
else:
return {**common_config, "background": "#1C1C1C", "foreground": "#ffffff",
"highlightcolor": "#898b8e", "justify": tk.LEFT}
if is_message_box:
temp_tooltip = tk.Toplevel(self.widget)
temp_tooltip.wm_overrideredirect(True)
temp_tooltip.withdraw()
label = tk.Label(temp_tooltip, **create_label_config())
label.pack()
temp_tooltip.update() if is_windows else temp_tooltip.update_idletasks()
x = self.widget.winfo_rootx() + (self.widget.winfo_width() // 2) - (temp_tooltip.winfo_reqwidth() // 2)
y = self.widget.winfo_rooty() + self.widget.winfo_height()
temp_tooltip.destroy()
else:
x, y, _, _ = self.widget.bbox("insert")
x += self.widget.winfo_rootx() + 25
y += self.widget.winfo_rooty() + 25
# Create the actual tooltip
self.tooltip = tk.Toplevel(self.widget)
self.tooltip.wm_overrideredirect(True)
self.tooltip.wm_geometry(f"+{x}+{y}")
label_config = create_label_config()
if not is_message_box:
label_config['padx'] = 10 # horizontal padding
label_config['pady'] = 10 # vertical padding
label_config["wraplength"] = 750
label = tk.Label(self.tooltip, **label_config)
label.pack()
if is_message_box:
self.tooltip.after(3000 if type(is_success_message) is bool else 2000, self.hidetip)
def hidetip(self):
if self.tooltip:
self.tooltip.destroy()
self.tooltip = None
class ListboxBatchFrame(tk.Frame):
def __init__(self, master=None, name="Listbox", command=None, image_sel=None, img_mapper=None):
super().__init__(master)
self.master = master
self.path_list = [] # A list to keep track of the paths
self.basename_to_path = {} # A dict to map basenames to paths
self.label = tk.Label(self, text=name, font=(MAIN_FONT_NAME, f"{FONT_SIZE_5}"), foreground=FG_COLOR)
self.label.pack(pady=(10, 8)) # add padding between label and listbox
self.input_button = ttk.Button(self, text=SELECT_INPUTS, command=self.select_input) # create button for selecting files
self.input_button.pack(pady=(0, 10)) # add padding between button and next widget
self.listbox = tk.Listbox(self, activestyle='dotbox', font=(MAIN_FONT_NAME, f"{FONT_SIZE_4}"), foreground='#cdd3ce', background='#101414', exportselection=0, width=70, height=15)
self.listbox.pack(fill="both", expand=True)
self.button_frame = tk.Frame(self)
self.button_frame.pack()
self.up_button = ttk.Button(self.button_frame, image=img_mapper["up"], command=self.move_up)
self.up_button.grid(row=0, column=0)
self.down_button = ttk.Button(self.button_frame, image=img_mapper["down"], command=self.move_down)
self.down_button.grid(row=0, column=1)
if command and image_sel:
self.move_button = ttk.Button(self.button_frame, image=image_sel, command=command)
self.move_button.grid(row=0, column=2)