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# GUI modules
import tkinter as tk
import tkinter.ttk as ttk
import tkinter.messagebox
import tkinter.filedialog
import tkinter.font
from datetime import datetime
# Images
from PIL import Image
from PIL import ImageTk
import pickle # Save Data
# Other Modules
import subprocess # Run python file
# Pathfinding
import pathlib
import sys
import os
from collections import defaultdict
# Used for live text displaying
import queue
import threading # Run the algorithm inside a thread
import inference_v2
import inference_v4
# 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
instrumentalModels_dir = os.path.join(base_path, 'models')
stackedModels_dir = os.path.join(base_path, 'models')
logo_path = os.path.join(base_path, 'img', 'UVR-logo.png')
refresh_path = os.path.join(base_path, 'img', 'refresh.png')
DEFAULT_DATA = {
'export_path': '',
'gpu': False,
'postprocess': False,
'tta': False,
'output_image': False,
'sr': 44100,
'hop_length': 1024,
'window_size': 512,
'n_fft': 2048,
'stack': False,
'stackPasses': 1,
'stackOnly': False,
'saveAllStacked': False,
'modelFolder': False,
'aiModel': 'v4',
'useModel': 'instrumental',
'lastDir': None,
}
def open_image(path: str, size: tuple = None, keep_aspect: bool = True, rotate: int = 0) -> ImageTk.PhotoImage:
"""
Open the image on the path and apply given settings\n
Paramaters:
path(str):
Absolute path of the image
size(tuple):
first value - width
second value - height
keep_aspect(bool):
keep aspect ratio of image and resize
to maximum possible width and height
(maxima are given by size)
rotate(int):
clockwise rotation of image
Returns(ImageTk.PhotoImage):
Image of path
"""
img = Image.open(path).convert(mode='RGBA')
ratio = img.height/img.width
img = img.rotate(angle=-rotate)
if size is not None:
size = (int(size[0]), int(size[1]))
if keep_aspect:
img = img.resize((size[0], int(size[0] * ratio)), Image.ANTIALIAS)
else:
img = img.resize(size, Image.ANTIALIAS)
return ImageTk.PhotoImage(img)
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 get_model_values(model_name):
text = model_name.replace('.pth', '')
text_parts = text.split('_')[1:]
model_values = {}
for text_part in text_parts:
if 'sr' in text_part:
text_part = text_part.replace('sr', '')
if text_part.isdecimal():
try:
model_values['sr'] = int(text_part)
continue
except ValueError:
# Cannot convert string to int
pass
if 'hl' in text_part:
text_part = text_part.replace('hl', '')
if text_part.isdecimal():
try:
model_values['hop_length'] = int(text_part)
continue
except ValueError:
# Cannot convert string to int
pass
if 'w' in text_part:
text_part = text_part.replace('w', '')
if text_part.isdecimal():
try:
model_values['window_size'] = int(text_part)
continue
except ValueError:
# Cannot convert string to int
pass
if 'nf' in text_part:
text_part = text_part.replace('nf', '')
if text_part.isdecimal():
try:
model_values['n_fft'] = int(text_part)
continue
except ValueError:
# Cannot convert string to int
pass
return model_values
class ThreadSafeConsole(tk.Text):
"""
Text Widget which is thread safe for tkinter
"""
def __init__(self, master, **options):
tk.Text.__init__(self, master, **options)
self.queue = queue.Queue()
self.update_me()
def write(self, line):
self.queue.put(line)
def clear(self):
self.queue.put(None)
def update_me(self):
self.configure(state=tk.NORMAL)
try:
while 1:
line = self.queue.get_nowait()
if line is None:
self.delete(1.0, tk.END)
else:
self.insert(tk.END, str(line))
self.see(tk.END)
self.update_idletasks()
except queue.Empty:
pass
self.configure(state=tk.DISABLED)
self.after(100, self.update_me)
class MainWindow(tk.Tk):
# --Constants--
# Layout
IMAGE_HEIGHT = 140
FILEPATHS_HEIGHT = 90
OPTIONS_HEIGHT = 240
CONVERSIONBUTTON_HEIGHT = 35
COMMAND_HEIGHT = 200
PROGRESS_HEIGHT = 26
PADDING = 10
COL1_ROWS = 8
COL2_ROWS = 7
COL3_ROWS = 5
def __init__(self):
# Run the __init__ method on the tk.Tk class
super().__init__()
# Calculate window height
height = self.IMAGE_HEIGHT + self.FILEPATHS_HEIGHT + self.OPTIONS_HEIGHT
height += self.CONVERSIONBUTTON_HEIGHT + self.COMMAND_HEIGHT + self.PROGRESS_HEIGHT
height += self.PADDING * 5 # Padding
# --Window Settings--
self.title('Vocal Remover')
# Set Geometry and Center Window
self.geometry('{width}x{height}+{xpad}+{ypad}'.format(
width=590,
height=height,
xpad=int(self.winfo_screenwidth()/2 - 550/2),
ypad=int(self.winfo_screenheight()/2 - height/2 - 30)))
self.configure(bg='#000000') # Set background color to black
self.resizable(False, False)
self.update()
# --Variables--
self.logo_img = open_image(path=logo_path,
size=(self.winfo_width(), 9999))
self.refresh_img = open_image(path=refresh_path,
size=(20, 20))
self.instrumentalLabel_to_path = defaultdict(lambda: '')
self.stackedLabel_to_path = defaultdict(lambda: '')
self.lastInstrumentalModels = []
self.lastStackedModels = []
# -Tkinter Value Holders-
data = load_data()
# Paths
self.exportPath_var = tk.StringVar(value=data['export_path'])
self.inputPaths = []
# Processing Options
self.gpuConversion_var = tk.BooleanVar(value=data['gpu'])
self.postprocessing_var = tk.BooleanVar(value=data['postprocess'])
self.tta_var = tk.BooleanVar(value=data['tta'])
self.outputImage_var = tk.BooleanVar(value=data['output_image'])
# Models
self.instrumentalModel_var = tk.StringVar(value='')
self.stackedModel_var = tk.StringVar(value='')
# Stacked Options
self.stack_var = tk.BooleanVar(value=data['stack'])
self.stackLoops_var = tk.StringVar(value=data['stackPasses'])
self.stackOnly_var = tk.BooleanVar(value=data['stackOnly'])
self.saveAllStacked_var = tk.BooleanVar(value=data['saveAllStacked'])
self.modelFolder_var = tk.BooleanVar(value=data['modelFolder'])
# Constants
self.srValue_var = tk.StringVar(value=data['sr'])
self.hopValue_var = tk.StringVar(value=data['hop_length'])
self.winSize_var = tk.StringVar(value=data['window_size'])
self.nfft_var = tk.StringVar(value=data['n_fft'])
# AI model
self.aiModel_var = tk.StringVar(value=data['aiModel'])
self.last_aiModel = self.aiModel_var.get()
# Other
self.lastDir = data['lastDir'] # nopep8
self.progress_var = tk.IntVar(value=0)
# Font
self.font = tk.font.Font(family='Helvetica', size=9, weight='bold')
# --Widgets--
self.create_widgets()
self.configure_widgets()
self.place_widgets()
self.update_available_models()
self.update_states()
self.update_loop()
# -Widget Methods-
def create_widgets(self):
"""Create window widgets"""
self.title_Label = tk.Label(master=self, bg='black',
image=self.logo_img, compound=tk.TOP)
self.filePaths_Frame = tk.Frame(master=self, bg='black')
self.fill_filePaths_Frame()
self.options_Frame = tk.Frame(master=self, bg='black')
self.fill_options_Frame()
self.conversion_Button = ttk.Button(master=self,
text='Start Conversion',
command=self.start_conversion)
self.refresh_Button = ttk.Button(master=self,
image=self.refresh_img,
command=self.restart)
self.progressbar = ttk.Progressbar(master=self,
variable=self.progress_var)
self.command_Text = ThreadSafeConsole(master=self,
background='#a0a0a0',
borderwidth=0,)
self.command_Text.write(f'COMMAND LINE [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]') # nopep8
def configure_widgets(self):
"""Change widget styling and appearance"""
ttk.Style().configure('TCheckbutton', background='black',
font=self.font, foreground='white')
ttk.Style().configure('TRadiobutton', background='black',
font=self.font, foreground='white')
ttk.Style().configure('T', font=self.font, foreground='white')
def place_widgets(self):
"""Place main widgets"""
self.title_Label.place(x=-2, y=-2)
self.filePaths_Frame.place(x=10, y=self.IMAGE_HEIGHT, width=-20, height=self.FILEPATHS_HEIGHT,
relx=0, rely=0, relwidth=1, relheight=0)
self.options_Frame.place(x=25, y=self.IMAGE_HEIGHT + self.FILEPATHS_HEIGHT + self.PADDING, width=-50, height=self.OPTIONS_HEIGHT,
relx=0, rely=0, relwidth=1, relheight=0)
self.conversion_Button.place(x=10, y=self.IMAGE_HEIGHT + self.FILEPATHS_HEIGHT + self.OPTIONS_HEIGHT + self.PADDING*2, width=-20 - 40, height=self.CONVERSIONBUTTON_HEIGHT,
relx=0, rely=0, relwidth=1, relheight=0)
self.refresh_Button.place(x=-10 - 35, y=self.IMAGE_HEIGHT + self.FILEPATHS_HEIGHT + self.OPTIONS_HEIGHT + self.PADDING*2, width=35, height=self.CONVERSIONBUTTON_HEIGHT,
relx=1, rely=0, relwidth=0, relheight=0)
self.command_Text.place(x=15, y=self.IMAGE_HEIGHT + self.FILEPATHS_HEIGHT + self.OPTIONS_HEIGHT + self.CONVERSIONBUTTON_HEIGHT + self.PADDING*3, width=-30, height=self.COMMAND_HEIGHT,
relx=0, rely=0, relwidth=1, relheight=0)
self.progressbar.place(x=25, y=self.IMAGE_HEIGHT + self.FILEPATHS_HEIGHT + self.OPTIONS_HEIGHT + self.CONVERSIONBUTTON_HEIGHT + self.COMMAND_HEIGHT + self.PADDING*4, width=-50, height=self.PROGRESS_HEIGHT,
relx=0, rely=0, relwidth=1, relheight=0)
def fill_filePaths_Frame(self):
"""Fill Frame with neccessary widgets"""
# -Create Widgets-
# Save To Option
self.filePaths_saveTo_Button = ttk.Button(master=self.filePaths_Frame,
text='Save to',
command=self.open_export_filedialog)
self.filePaths_saveTo_Entry = ttk.Entry(master=self.filePaths_Frame,
textvariable=self.exportPath_var,
state=tk.DISABLED
)
# Select Music Files Option
self.filePaths_musicFile_Button = ttk.Button(master=self.filePaths_Frame,
text='Select Your Audio File(s)',
command=self.open_file_filedialog)
self.filePaths_musicFile_Entry = ttk.Entry(master=self.filePaths_Frame,
text=self.inputPaths,
state=tk.DISABLED
)
# -Place Widgets-
# Save To Option
self.filePaths_saveTo_Button.place(x=0, y=5, width=0, height=-10,
relx=0, rely=0, relwidth=0.3, relheight=0.5)
self.filePaths_saveTo_Entry.place(x=10, y=7, width=-20, height=-14,
relx=0.3, rely=0, relwidth=0.7, relheight=0.5)
# Select Music Files Option
self.filePaths_musicFile_Button.place(x=0, y=5, width=0, height=-10,
relx=0, rely=0.5, relwidth=0.4, relheight=0.5)
self.filePaths_musicFile_Entry.place(x=10, y=7, width=-20, height=-14,
relx=0.4, rely=0.5, relwidth=0.6, relheight=0.5)
def fill_options_Frame(self):
"""Fill Frame with neccessary widgets"""
# -Create Widgets-
# -Column 1-
# GPU Selection
self.options_gpu_Checkbutton = ttk.Checkbutton(master=self.options_Frame,
text='GPU Conversion',
variable=self.gpuConversion_var,
)
# Postprocessing
self.options_post_Checkbutton = ttk.Checkbutton(master=self.options_Frame,
text='Post-Process',
variable=self.postprocessing_var,
)
# TTA
self.options_tta_Checkbutton = ttk.Checkbutton(master=self.options_Frame,
text='TTA',
variable=self.tta_var,
)
# Save Image
self.options_image_Checkbutton = ttk.Checkbutton(master=self.options_Frame,
text='Output Image',
variable=self.outputImage_var,
)
# Stack Loops
self.options_stack_Checkbutton = ttk.Checkbutton(master=self.options_Frame,
text='Stack Passes',
variable=self.stack_var,
)
self.options_stack_Entry = ttk.Entry(master=self.options_Frame,
textvariable=self.stackLoops_var,)
# Stack Only
self.options_stackOnly_Checkbutton = ttk.Checkbutton(master=self.options_Frame,
text='Stack Conversion Only',
variable=self.stackOnly_var,
)
# Save All Stacked Outputs
self.options_saveStack_Checkbutton = ttk.Checkbutton(master=self.options_Frame,
text='Save All Stacked Outputs',
variable=self.saveAllStacked_var,
)
self.options_modelFolder_Checkbutton = ttk.Checkbutton(master=self.options_Frame,
text='Model Test Mode',
variable=self.modelFolder_var,
)
# -Column 2-
# SR
self.options_sr_Entry = ttk.Entry(master=self.options_Frame,
textvariable=self.srValue_var,)
self.options_sr_Label = tk.Label(master=self.options_Frame,
text='SR', anchor=tk.W,
background='#63605f', font=self.font, foreground='white', relief="sunken")
# HOP LENGTH
self.options_hop_Entry = ttk.Entry(master=self.options_Frame,
textvariable=self.hopValue_var,)
self.options_hop_Label = tk.Label(master=self.options_Frame,
text='HOP LENGTH', anchor=tk.W,
background='#63605f', font=self.font, foreground='white', relief="sunken")
# WINDOW SIZE
self.options_winSize_Entry = ttk.Entry(master=self.options_Frame,
textvariable=self.winSize_var,)
self.options_winSize_Label = tk.Label(master=self.options_Frame,
text='WINDOW SIZE', anchor=tk.W,
background='#63605f', font=self.font, foreground='white', relief="sunken")
# N_FFT
self.options_nfft_Entry = ttk.Entry(master=self.options_Frame,
textvariable=self.nfft_var,)
self.options_nfft_Label = tk.Label(master=self.options_Frame,
text='N_FFT', anchor=tk.W,
background='#63605f', font=self.font, foreground='white', relief="sunken")
# AI model
self.options_aiModel_Label = tk.Label(master=self.options_Frame,
text='Choose AI Engine', anchor=tk.CENTER,
background='#63605f', font=self.font, foreground='white', relief="sunken")
self.options_aiModel_Optionmenu = ttk.OptionMenu(self.options_Frame,
self.aiModel_var,
None, 'v2', 'v4',)
# "Save to", "Select Your Audio File(s)"", and "Start Conversion" Button Style
s = ttk.Style()
s.configure('TButton', background='blue', foreground='black', font=('Verdana', '9', 'bold'), relief="sunken")
# -Column 3-
# Choose Instrumental Model
self.options_instrumentalModel_Label = tk.Label(master=self.options_Frame,
text='Choose Instrumental Model',
background='#a7a7a7', font=self.font, relief="ridge")
self.options_instrumentalModel_Optionmenu = ttk.OptionMenu(self.options_Frame,
self.instrumentalModel_var)
# Choose Stacked Model
self.options_stackedModel_Label = tk.Label(master=self.options_Frame,
text='Choose Stacked Model',
background='#a7a7a7', font=self.font, relief="ridge")
self.options_stackedModel_Optionmenu = ttk.OptionMenu(self.options_Frame,
self.stackedModel_var,)
self.options_model_Button = ttk.Button(master=self.options_Frame,
text='Add New Model(s)',
style="Bold.TButton",
command=self.open_newModel_filedialog)
# -Place Widgets-
# -Column 1-
self.options_gpu_Checkbutton.place(x=0, y=0, width=0, height=0,
relx=0, rely=0, relwidth=1/3, relheight=1/self.COL1_ROWS)
self.options_post_Checkbutton.place(x=0, y=0, width=0, height=0,
relx=0, rely=1/self.COL1_ROWS, relwidth=1/3, relheight=1/self.COL1_ROWS)
self.options_tta_Checkbutton.place(x=0, y=0, width=0, height=0,
relx=0, rely=2/self.COL1_ROWS, relwidth=1/3, relheight=1/self.COL1_ROWS)
self.options_image_Checkbutton.place(x=0, y=0, width=0, height=0,
relx=0, rely=3/self.COL1_ROWS, relwidth=1/3, relheight=1/self.COL1_ROWS)
# Stacks
self.options_stack_Checkbutton.place(x=0, y=0, width=0, height=0,
relx=0, rely=4/self.COL1_ROWS, relwidth=1/3/4*3, relheight=1/self.COL1_ROWS)
self.options_stack_Entry.place(x=0, y=3, width=0, height=-6,
relx=1/3/4*2.4, rely=4/self.COL1_ROWS, relwidth=1/3/4*0.9, relheight=1/self.COL1_ROWS)
self.options_stackOnly_Checkbutton.place(x=0, y=0, width=0, height=0,
relx=0, rely=5/self.COL1_ROWS, relwidth=1/3, relheight=1/self.COL1_ROWS)
self.options_saveStack_Checkbutton.place(x=0, y=0, width=0, height=0,
relx=0, rely=6/self.COL1_ROWS, relwidth=1/3, relheight=1/self.COL1_ROWS)
# Model Folder
self.options_modelFolder_Checkbutton.place(x=0, y=0, width=0, height=0,
relx=0, rely=7/self.COL1_ROWS, relwidth=1/3, relheight=1/self.COL1_ROWS)
# -Column 2-
# SR
self.options_sr_Label.place(x=5, y=4, width=5, height=-8,
relx=1/3, rely=0, relwidth=1/3/2, relheight=1/self.COL2_ROWS)
self.options_sr_Entry.place(x=15, y=4, width=5, height=-8,
relx=1/3 + 1/3/2, rely=0, relwidth=1/3/4, relheight=1/self.COL2_ROWS)
# HOP LENGTH
self.options_hop_Label.place(x=5, y=4, width=5, height=-8,
relx=1/3, rely=1/self.COL2_ROWS, relwidth=1/3/2, relheight=1/self.COL2_ROWS)
self.options_hop_Entry.place(x=15, y=4, width=5, height=-8,
relx=1/3 + 1/3/2, rely=1/self.COL2_ROWS, relwidth=1/3/4, relheight=1/self.COL2_ROWS)
# WINDOW SIZE
self.options_winSize_Label.place(x=5, y=4, width=5, height=-8,
relx=1/3, rely=2/self.COL2_ROWS, relwidth=1/3/2, relheight=1/self.COL2_ROWS)
self.options_winSize_Entry.place(x=15, y=4, width=5, height=-8,
relx=1/3 + 1/3/2, rely=2/self.COL2_ROWS, relwidth=1/3/4, relheight=1/self.COL2_ROWS)
# N_FFT
self.options_nfft_Label.place(x=5, y=4, width=5, height=-8,
relx=1/3, rely=3/self.COL2_ROWS, relwidth=1/3/2, relheight=1/self.COL2_ROWS)
self.options_nfft_Entry.place(x=15, y=4, width=5, height=-8,
relx=1/3 + 1/3/2, rely=3/self.COL2_ROWS, relwidth=1/3/4, relheight=1/self.COL2_ROWS)
# AI model
self.options_aiModel_Label.place(x=5, y=-5, width=-30, height=-8,
relx=1/3, rely=5/self.COL2_ROWS, relwidth=1/3, relheight=1/self.COL2_ROWS)
self.options_aiModel_Optionmenu.place(x=5, y=-5, width=-30, height=-8,
relx=1/3, rely=6/self.COL2_ROWS, relwidth=1/3, relheight=1/self.COL2_ROWS)
# -Column 3-
# Choose Model
self.options_instrumentalModel_Label.place(x=0, y=0, width=0, height=-10,
relx=2/3, rely=0, relwidth=1/3, relheight=1/self.COL3_ROWS)
self.options_instrumentalModel_Optionmenu.place(x=15, y=-4, width=-30, height=-13,
relx=2/3, rely=1/self.COL3_ROWS, relwidth=1/3, relheight=1/self.COL3_ROWS)
self.options_stackedModel_Label.place(x=0, y=0, width=0, height=-10,
relx=2/3, rely=2/self.COL3_ROWS, relwidth=1/3, relheight=1/self.COL3_ROWS)
self.options_stackedModel_Optionmenu.place(x=15, y=-4, width=-30, height=-13,
relx=2/3, rely=3/self.COL3_ROWS, relwidth=1/3, relheight=1/self.COL3_ROWS)
self.options_model_Button.place(x=15, y=3, width=-30, height=-8,
relx=2/3, rely=4/self.COL3_ROWS, relwidth=1/3, relheight=1/self.COL3_ROWS)
# -Update Binds-
self.options_stackOnly_Checkbutton.configure(command=self.update_states) # nopep8
self.options_stack_Checkbutton.configure(command=self.update_states) # nopep8
self.options_stack_Entry.bind('<FocusOut>',
lambda e: self.update_states())
# Model name decoding
self.instrumentalModel_var.trace_add('write',
lambda *args: self.decode_modelNames())
self.stackedModel_var.trace_add('write',
lambda *args: self.decode_modelNames())
# Model deselect
self.aiModel_var.trace_add('write',
lambda *args: self.deselect_models())
# Opening filedialogs
def open_file_filedialog(self):
"""Make user select music files"""
if self.lastDir is not None:
if not os.path.isdir(self.lastDir):
self.lastDir = None
paths = tk.filedialog.askopenfilenames(
parent=self,
title=f'Select Music Files',
initialfile='',
initialdir=self.lastDir,
)
if paths: # Path selected
self.inputPaths = paths
# Change the entry text
self.filePaths_musicFile_Entry.configure(state=tk.NORMAL)
self.filePaths_musicFile_Entry.delete(0, tk.END)
self.filePaths_musicFile_Entry.insert(0, self.inputPaths)
self.filePaths_musicFile_Entry.configure(state=tk.DISABLED)
self.lastDir = os.path.dirname(paths[0])
def open_export_filedialog(self):
"""Make user select a folder to export the converted files in"""
path = tk.filedialog.askdirectory(
parent=self,
title=f'Select Folder',)
if path: # Path selected
self.exportPath_var.set(path)
def open_newModel_filedialog(self):
"""Let user paste a ".pth" model to use for the vocal seperation"""
os.startfile('models')
def start_conversion(self):
"""
Start the conversion for all the given mp3 and wav files
"""
# -Get all variables-
export_path = self.exportPath_var.get()
instrumentalModel_path = self.instrumentalLabel_to_path[self.instrumentalModel_var.get()] # nopep8
stackedModel_path = self.stackedLabel_to_path[self.stackedModel_var.get()] # nopep8
# Get constants
instrumental = get_model_values(self.instrumentalModel_var.get())
stacked = get_model_values(self.stackedModel_var.get())
try:
if [bool(instrumental), bool(stacked)].count(True) == 2:
sr = DEFAULT_DATA['sr']
hop_length = DEFAULT_DATA['hop_length']
window_size = DEFAULT_DATA['window_size']
n_fft = DEFAULT_DATA['n_fft']
else:
sr = int(self.srValue_var.get())
hop_length = int(self.hopValue_var.get())
window_size = int(self.winSize_var.get())
n_fft = int(self.nfft_var.get())
stackPasses = int(self.stackLoops_var.get())
except ValueError: # Non integer was put in entry box
tk.messagebox.showwarning(master=self,
title='Invalid Input',
message='Please make sure you only input integer numbers!')
return
except SyntaxError: # Non integer was put in entry box
tk.messagebox.showwarning(master=self,
title='Invalid Music File',
message='You have selected an invalid music file!\nPlease make sure that your files still exist and ends with either ".mp3", ".mp4", ".m4a", ".flac", ".wav"')
return
# -Check for invalid inputs-
if not any([(os.path.isfile(path) and path.endswith(('.mp3', '.mp4', '.m4a', '.flac', '.wav')))
for path in self.inputPaths]):
tk.messagebox.showwarning(master=self,
title='Invalid Music File',
message='You have selected an invalid music file!\nPlease make sure that your files still exist and ends with either ".mp3", ".mp4", ".m4a", ".flac", ".wav"')
return
if not os.path.isdir(export_path):
tk.messagebox.showwarning(master=self,
title='Invalid Export Directory',
message='You have selected an invalid export directory!\nPlease make sure that your directory still exists!')
return
if not self.stackOnly_var.get():
if not os.path.isfile(instrumentalModel_path):
tk.messagebox.showwarning(master=self,
title='Invalid Instrumental Model File',
message='You have selected an invalid instrumental model file!\nPlease make sure that your model file still exists!')
return
if (self.stackOnly_var.get() or
stackPasses > 0):
if not os.path.isfile(stackedModel_path):
tk.messagebox.showwarning(master=self,
title='Invalid Stacked Model File',
message='You have selected an invalid stacked model file!\nPlease make sure that your model file still exists!')
return
# -Save Data-
save_data(data={
'export_path': export_path,
'gpu': self.gpuConversion_var.get(),
'postprocess': self.postprocessing_var.get(),
'tta': self.tta_var.get(),
'output_image': self.outputImage_var.get(),
'stack': self.stack_var.get(),
'stackOnly': self.stackOnly_var.get(),
'stackPasses': stackPasses,
'saveAllStacked': self.saveAllStacked_var.get(),
'sr': sr,
'hop_length': hop_length,
'window_size': window_size,
'n_fft': n_fft,
'useModel': 'instrumental', # Always instrumental
'lastDir': self.lastDir,
'modelFolder': self.modelFolder_var.get(),
'aiModel': self.aiModel_var.get(),
})
if self.aiModel_var.get() == 'v2':
inference = inference_v2
elif self.aiModel_var.get() == 'v4':
inference = inference_v4
else:
raise TypeError('This error should not occur.')
# -Run the algorithm-
threading.Thread(target=inference.main,
kwargs={
# Paths
'input_paths': self.inputPaths,
'export_path': export_path,
# Processing Options
'gpu': 0 if self.gpuConversion_var.get() else -1,
'postprocess': self.postprocessing_var.get(),
'tta': self.tta_var.get(), # not needed for v2
'output_image': self.outputImage_var.get(),
# Models
'instrumentalModel': instrumentalModel_path,
'vocalModel': '', # Always not needed
'stackModel': stackedModel_path,
'useModel': 'instrumental', # Always instrumental
# Stack Options
'stackPasses': stackPasses,
'stackOnly': self.stackOnly_var.get(),
'saveAllStacked': self.saveAllStacked_var.get(),
# Model Folder
'modelFolder': self.modelFolder_var.get(),
# Constants
'sr': sr,
'hop_length': hop_length,
'window_size': window_size,
'n_fft': n_fft, # not needed for v2
# Other Variables (Tkinter)
'window': self,
'text_widget': self.command_Text,
'button_widget': self.conversion_Button,
'progress_var': self.progress_var,
},
daemon=True
).start()
# Models
def decode_modelNames(self):
"""
Enable/Disable the 4 constants based on the selected model names
"""
# Check state of model selectors
instrumental_selectable = bool(str(self.options_instrumentalModel_Optionmenu.cget('state')) == 'normal')
stacked_selectable = bool(str(self.options_stackedModel_Optionmenu.cget('state')) == 'normal')
# Extract data from models name
instrumental = get_model_values(self.instrumentalModel_var.get())
stacked = get_model_values(self.stackedModel_var.get())
# Assign widgets to constants
widgetsVars = {
'sr': [self.options_sr_Entry, self.srValue_var],
'hop_length': [self.options_hop_Entry, self.hopValue_var],
'window_size': [self.options_winSize_Entry, self.winSize_var],
'n_fft': [self.options_nfft_Entry, self.nfft_var],
}
# Loop through each constant (key) and its widgets
for key, (widget, var) in widgetsVars.items():
if stacked_selectable:
# Stacked model can be selected
if key in stacked.keys():
if (key in stacked.keys() and
not instrumental_selectable):
# Only stacked selectable
widget.configure(state=tk.DISABLED)
var.set(stacked[key])
continue
elif (key in instrumental.keys() and
instrumental_selectable):
# Both models have set constants
widget.configure(state=tk.DISABLED)
var.set('%d/%d' % (instrumental[key], stacked[key]))
continue
else:
# Stacked model can not be selected
if (key in instrumental.keys() and
instrumental_selectable):
widget.configure(state=tk.DISABLED)
var.set(instrumental[key])
continue
# If widget is already enabled, no need to reset the value
if str(widget.cget('state')) != 'normal':
widget.configure(state=tk.NORMAL)
var.set(DEFAULT_DATA[key])
def update_loop(self):
"""Update the dropdown menu"""
self.update_available_models()
self.after(3000, self.update_loop)
def update_available_models(self):
"""
Loop through every model (.pth) in the models directory
and add to the select your model list
"""
temp_instrumentalModels_dir = os.path.join(instrumentalModels_dir, self.aiModel_var.get(), 'Instrumental Models') # nopep8
temp_stackedModels_dir = os.path.join(stackedModels_dir, self.aiModel_var.get(), 'Stacked Models')
# Instrumental models
new_InstrumentalModels = os.listdir(temp_instrumentalModels_dir)
if new_InstrumentalModels != self.lastInstrumentalModels:
self.instrumentalLabel_to_path.clear()
self.options_instrumentalModel_Optionmenu['menu'].delete(0, 'end')
for file_name in new_InstrumentalModels:
if file_name.endswith('.pth'):
# Add Radiobutton to the Options Menu
self.options_instrumentalModel_Optionmenu['menu'].add_radiobutton(label=file_name,
command=tk._setit(self.instrumentalModel_var, file_name))
# Link the files name to its absolute path
self.instrumentalLabel_to_path[file_name] = os.path.join(temp_instrumentalModels_dir, file_name) # nopep8
self.lastInstrumentalModels = new_InstrumentalModels
# Stacked models
new_stackedModels = os.listdir(temp_stackedModels_dir)
if new_stackedModels != self.lastStackedModels:
self.stackedLabel_to_path.clear()
self.options_stackedModel_Optionmenu['menu'].delete(0, 'end')
for file_name in new_stackedModels:
if file_name.endswith('.pth'):
# Add Radiobutton to the Options Menu
self.options_stackedModel_Optionmenu['menu'].add_radiobutton(label=file_name,
command=tk._setit(self.stackedModel_var, file_name))
# Link the files name to its absolute path
self.stackedLabel_to_path[file_name] = os.path.join(temp_stackedModels_dir, file_name) # nopep8
self.lastStackedModels = new_stackedModels
def update_states(self):
"""
Vary the states for all widgets based
on certain selections
"""
try:
stackLoops = int(self.stackLoops_var.get())
except ValueError:
stackLoops = 0
# Stack Passes
if self.stack_var.get():
self.options_stack_Entry.configure(state=tk.NORMAL)
if stackLoops <= 0:
self.stackLoops_var.set(1)
stackLoops = 1
else:
self.options_stack_Entry.configure(state=tk.DISABLED)
self.stackLoops_var.set(0)
stackLoops = 0
# Stack Only and Save All Outputs
if stackLoops > 0:
self.options_stackOnly_Checkbutton.configure(state=tk.NORMAL)
self.options_saveStack_Checkbutton.configure(state=tk.NORMAL)
else:
self.options_stackOnly_Checkbutton.configure(state=tk.DISABLED)
self.options_saveStack_Checkbutton.configure(state=tk.DISABLED)
self.saveAllStacked_var.set(False)
self.stackOnly_var.set(False)
# Models
if self.stackOnly_var.get():
# Instrumental Model
self.options_instrumentalModel_Label.configure(foreground='#777')
self.options_instrumentalModel_Optionmenu.configure(state=tk.DISABLED) # nopep8
self.instrumentalModel_var.set('')
# Stack Model
self.options_stackedModel_Label.configure(foreground='#000')
self.options_stackedModel_Optionmenu.configure(state=tk.NORMAL) # nopep8
else:
# Instrumental Model
self.options_instrumentalModel_Label.configure(foreground='#000')
self.options_instrumentalModel_Optionmenu.configure(state=tk.NORMAL) # nopep8
self.instrumentalModel_var.set('')
# Stack Model
if stackLoops > 0:
self.options_stackedModel_Label.configure(foreground='#000')
self.options_stackedModel_Optionmenu.configure(state=tk.NORMAL) # nopep8
else:
self.options_stackedModel_Label.configure(foreground='#777')
self.options_stackedModel_Optionmenu.configure(state=tk.DISABLED) # nopep8
self.stackedModel_var.set('')
if self.aiModel_var.get() == 'v2':
self.options_tta_Checkbutton.configure(state=tk.DISABLED)
self.options_nfft_Label.place_forget()
self.options_nfft_Entry.place_forget()
else:
self.options_tta_Checkbutton.configure(state=tk.NORMAL)
self.options_nfft_Label.place(x=5, y=4, width=5, height=-8,
relx=1/3, rely=3/self.COL2_ROWS, relwidth=1/3/2, relheight=1/self.COL2_ROWS)
self.options_nfft_Entry.place(x=15, y=4, width=5, height=-8,
relx=1/3 + 1/3/2, rely=3/self.COL2_ROWS, relwidth=1/3/4, relheight=1/self.COL2_ROWS)
self.decode_modelNames()
def deselect_models(self):
"""
Run this method on version change
"""
if self.aiModel_var.get() == self.last_aiModel:
return
else:
self.last_aiModel = self.aiModel_var.get()
self.instrumentalModel_var.set('')
self.stackedModel_var.set('')
self.srValue_var.set(DEFAULT_DATA['sr'])
self.hopValue_var.set(DEFAULT_DATA['hop_length'])
self.winSize_var.set(DEFAULT_DATA['window_size'])
self.nfft_var.set(DEFAULT_DATA['n_fft'])
self.update_available_models()
self.update_states()
def restart(self):
"""
Restart the application after asking for confirmation
"""
proceed = tk.messagebox.askyesno(title='Confirmation',
message='The application will restart and lose unsaved data. Do you wish to proceed?')
if proceed:
subprocess.Popen(f'python "{__file__}"', shell=True)
exit()
if __name__ == "__main__":
root = MainWindow()
root.mainloop()

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import argparse
import os
import cv2
import librosa
import numpy as np
import soundfile as sf
from tqdm import tqdm
from lib_v2 import dataset
from lib_v2 import nets
from lib_v2 import spec_utils
import torch
# Variable manipulation and command line text parsing
from collections import defaultdict
import tkinter as tk
import time # Timer
import traceback # Error Message Recent Calls
class Namespace:
"""
Replaces ArgumentParser
"""
def __init__(self, **kwargs):
self.__dict__.update(kwargs)
data = {
# Paths
'input_paths': None,
'export_path': None,
# Processing Options
'gpu': -1,
'postprocess': True,
'tta': True,
'output_image': True,
# Models
'instrumentalModel': None,
'vocalModel': None,
'stackModel': None,
'useModel': None,
# Stack Options
'stackPasses': 0,
'stackOnly': False,
'saveAllStacked': False,
# Model Folder
'modelFolder': False,
# Constants
'sr': 44_100,
'hop_length': 1_024,
'window_size': 512,
'n_fft': 2_048,
}
default_sr = data['sr']
default_hop_length = data['hop_length']
default_window_size = data['window_size']
default_n_fft = data['n_fft']
def update_progress(progress_var, total_files, total_loops, file_num, loop_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 / total_loops) * (loop_num + step)
progress_var.set(progress)
def get_baseText(total_files, total_loops, file_num, loop_num):
"""Create the base text for the command widget"""
text = 'File {file_num}/{total_files}:{loop} '.format(file_num=file_num,
total_files=total_files,
loop='' if total_loops <= 1 else f' ({loop_num+1}/{total_loops})')
return text
def update_constants(model_name):
"""
Decode the conversion settings from the model's name
"""
global data
text = model_name.replace('.pth', '')
text_parts = text.split('_')[1:]
# First set everything to default ->
# If file name is not decodeable (invalid or no text_parts), constants stay at default
data['sr'] = default_sr
data['hop_length'] = default_hop_length
data['window_size'] = default_window_size
data['n_fft'] = default_n_fft
for text_part in text_parts:
if 'sr' in text_part:
text_part = text_part.replace('sr', '')
if text_part.isdecimal():
try:
data['sr'] = int(text_part)
continue
except ValueError:
# Cannot convert string to int
pass
if 'hl' in text_part:
text_part = text_part.replace('hl', '')
if text_part.isdecimal():
try:
data['hop_length'] = int(text_part)
continue
except ValueError:
# Cannot convert string to int
pass
if 'w' in text_part:
text_part = text_part.replace('w', '')
if text_part.isdecimal():
try:
data['window_size'] = int(text_part)
continue
except ValueError:
# Cannot convert string to int
pass
if 'nf' in text_part:
text_part = text_part.replace('nf', '')
if text_part.isdecimal():
try:
data['n_fft'] = int(text_part)
continue
except ValueError:
# Cannot convert string to int
pass
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] + '-'
# -Vocal-
elif os.path.isfile(data['vocalModel']):
modelFolderName += os.path.splitext(os.path.basename(data['vocalModel']))[0] + '-'
# -Stack-
if os.path.isfile(data['stackModel']):
modelFolderName += os.path.splitext(os.path.basename(data['stackModel']))[0]
else:
modelFolderName = modelFolderName[:-1]
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):
def load_models():
text_widget.write('Loading models...\n') # nopep8 Write Command Text
models = defaultdict(lambda: None)
devices = defaultdict(lambda: None)
# -Instrumental-
if os.path.isfile(data['instrumentalModel']):
device = torch.device('cpu')
model = nets.CascadedASPPNet()
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)
models['instrumental'] = model
devices['instrumental'] = device
# -Vocal-
elif os.path.isfile(data['vocalModel']):
device = torch.device('cpu')
model = nets.CascadedASPPNet()
model.load_state_dict(torch.load(data['vocalModel'],
map_location=device))
if torch.cuda.is_available() and data['gpu'] >= 0:
device = torch.device('cuda:{}'.format(data['gpu']))
model.to(device)
models['vocal'] = model
devices['vocal'] = device
# -Stack-
if os.path.isfile(data['stackModel']):
device = torch.device('cpu')
model = nets.CascadedASPPNet()
model.load_state_dict(torch.load(data['stackModel'],
map_location=device))
if torch.cuda.is_available() and data['gpu'] >= 0:
device = torch.device('cuda:{}'.format(data['gpu']))
model.to(device)
models['stack'] = model
devices['stack'] = device
text_widget.write('Done!\n')
return models, devices
def load_wave_source():
X, sr = librosa.load(music_file,
data['sr'],
False,
dtype=np.float32,
res_type='kaiser_fast')
return X, sr
def stft_wave_source(X, model, device):
X = spec_utils.calc_spec(X, data['hop_length'])
X, phase = np.abs(X), np.exp(1.j * np.angle(X))
coeff = X.max()
X /= coeff
offset = model.offset
l, r, roi_size = dataset.make_padding(
X.shape[2], data['window_size'], offset)
X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode='constant')
X_roll = np.roll(X_pad, roi_size // 2, axis=2)
model.eval()
with torch.no_grad():
masks = []
masks_roll = []
length = int(np.ceil(X.shape[2] / roi_size))
for i in tqdm(range(length)):
update_progress(**progress_kwargs,
step=0.1 + 0.5*(i/(length - 1)))
start = i * roi_size
X_window = torch.from_numpy(np.asarray([
X_pad[:, :, start:start + data['window_size']],
X_roll[:, :, start:start + data['window_size']]
])).to(device)
pred = model.predict(X_window)
pred = pred.detach().cpu().numpy()
masks.append(pred[0])
masks_roll.append(pred[1])
mask = np.concatenate(masks, axis=2)[:, :, :X.shape[2]]
mask_roll = np.concatenate(masks_roll, axis=2)[
:, :, :X.shape[2]]
mask = (mask + np.roll(mask_roll, -roi_size // 2, axis=2)) / 2
if data['postprocess']:
vocal = X * (1 - mask) * coeff
mask = spec_utils.mask_uninformative(mask, vocal)
inst = X * mask * coeff
vocal = X * (1 - mask) * coeff
return inst, vocal, phase, mask
def invert_instrum_vocal(inst, vocal, phase):
wav_instrument = spec_utils.spec_to_wav(inst, phase, data['hop_length']) # nopep8
wav_vocals = spec_utils.spec_to_wav(vocal, phase, data['hop_length']) # nopep8
return wav_instrument, wav_vocals
def save_files(wav_instrument, wav_vocals):
"""Save output music files"""
vocal_name = None
instrumental_name = None
folder = ''
# Get the Suffix Name
if (not loop_num or
loop_num == (total_loops - 1)): # First or Last Loop
if data['stackOnly']:
if loop_num == (total_loops - 1): # Last Loop
if not (total_loops - 1): # Only 1 Loop
vocal_name = '(Vocals)'
instrumental_name = '(Instrumental)'
else:
vocal_name = '(Vocal_Final_Stacked_Output)'
instrumental_name = '(Instrumental_Final_Stacked_Output)'
elif data['useModel'] == 'instrumental':
if not loop_num: # First Loop
vocal_name = '(Vocals)'
if loop_num == (total_loops - 1): # Last Loop
if not (total_loops - 1): # Only 1 Loop
instrumental_name = '(Instrumental)'
else:
instrumental_name = '(Instrumental_Final_Stacked_Output)'
elif data['useModel'] == 'vocal':
if not loop_num: # First Loop
instrumental_name = '(Instrumental)'
if loop_num == (total_loops - 1): # Last Loop
if not (total_loops - 1): # Only 1 Loop
vocal_name = '(Vocals)'
else:
vocal_name = '(Vocals_Final_Stacked_Output)'
if data['useModel'] == 'vocal':
# Reverse names
vocal_name, instrumental_name = instrumental_name, vocal_name
elif data['saveAllStacked']:
folder = os.path.splitext(os.path.basename(base_name))[0] + ' Stacked Outputs' # nopep8
folder = os.path.basename(folder) + '/'
folder_path = os.path.dirname(base_name)
folder_path = os.path.join(folder_path, folder)
if not os.path.isdir(folder_path):
os.mkdir(folder_path)
if data['stackOnly']:
vocal_name = f'(Vocal_{loop_num}_Stacked_Output)'
instrumental_name = f'(Instrumental_{loop_num}_Stacked_Output)'
elif (data['useModel'] == 'vocal' or
data['useModel'] == 'instrumental'):
vocal_name = f'(Vocals_{loop_num}_Stacked_Output)'
instrumental_name = f'(Instrumental_{loop_num}_Stacked_Output)'
if data['useModel'] == 'vocal':
# 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.T, sr)
appendModelFolderName = modelFolderName.replace('/', '_')
# -Save files-
# Instrumental
if instrumental_name is not None:
instrumental_path = '{base_path}/{folder}{file_name}.wav'.format(
base_path=os.path.dirname(base_name),
folder=folder,
file_name=f'{os.path.basename(base_name)}_{instrumental_name}{appendModelFolderName}',
)
sf.write(instrumental_path,
wav_instrument.T, sr)
# Vocal
if vocal_name is not None:
vocal_path = '{base_path}/{folder}{file_name}.wav'.format(
base_path=os.path.dirname(base_name),
folder=folder,
file_name=f'{os.path.basename(base_name)}_{vocal_name}{appendModelFolderName}',
)
sf.write(vocal_path,
wav_vocals.T, sr)
def output_image():
norm_mask = np.uint8((1 - mask) * 255).transpose(1, 2, 0)
norm_mask = np.concatenate([
np.max(norm_mask, axis=2, keepdims=True),
norm_mask], axis=2)[::-1]
_, bin_mask = cv2.imencode('.png', norm_mask)
text_widget.write(base_text + 'Saving Mask...\n') # nopep8 Write Command Text
with open(f'{base_name}_(Mask).png', mode='wb') as f:
bin_mask.tofile(f)
data.update(kwargs)
# Update default settings
global default_sr
global default_hop_length
global default_window_size
global default_n_fft
default_sr = data['sr']
default_hop_length = data['hop_length']
default_window_size = data['window_size']
default_n_fft = data['n_fft']
stime = time.perf_counter()
progress_var.set(0)
text_widget.clear()
button_widget.configure(state=tk.DISABLED) # Disable Button
models, devices = load_models()
modelFolderName = determineModelFolderName()
if modelFolderName:
folder_path = f'{data["export_path"]}{modelFolderName}'
if not os.path.isdir(folder_path):
os.mkdir(folder_path)
# Determine Loops
total_loops = data['stackPasses']
if not data['stackOnly']:
total_loops += 1
for file_num, music_file in enumerate(data['input_paths'], start=1):
try:
# Determine File Name
base_name = f'{data["export_path"]}{modelFolderName}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
for loop_num in range(total_loops):
# -Determine which model will be used-
if not loop_num:
# First Iteration
if data['stackOnly']:
if os.path.isfile(data['stackModel']):
model_name = os.path.basename(data['stackModel'])
model = models['stack']
device = devices['stack']
else:
raise ValueError(f'Selected stack only model, however, stack model path file cannot be found\nPath: "{data["stackModel"]}"') # nopep8
else:
model_name = os.path.basename(data[f'{data["useModel"]}Model'])
model = models[data['useModel']]
device = devices[data['useModel']]
else:
model_name = os.path.basename(data['stackModel'])
# Every other iteration
model = models['stack']
device = devices['stack']
# Reference new music file
music_file = 'temp.wav'
# -Get text and update progress-
base_text = get_baseText(total_files=len(data['input_paths']),
total_loops=total_loops,
file_num=file_num,
loop_num=loop_num)
progress_kwargs = {'progress_var': progress_var,
'total_files': len(data['input_paths']),
'total_loops': total_loops,
'file_num': file_num,
'loop_num': loop_num}
update_progress(**progress_kwargs,
step=0)
update_constants(model_name)
# -Go through the different steps of seperation-
# Wave source
text_widget.write(base_text + 'Loading wave source...\n') # nopep8 Write Command Text
X, sr = load_wave_source()
text_widget.write(base_text + 'Done!\n') # nopep8 Write Command Text
update_progress(**progress_kwargs,
step=0.1)
# Stft of wave source
text_widget.write(base_text + 'Stft of wave source...\n') # nopep8 Write Command Text
inst, vocal, phase, mask = stft_wave_source(X, model, device)
text_widget.write(base_text + 'Done!\n') # nopep8 Write Command Text
update_progress(**progress_kwargs,
step=0.6)
# Inverse stft
text_widget.write(base_text + 'Inverse stft of instruments and vocals...\n') # nopep8 Write Command Text
wav_instrument, wav_vocals = invert_instrum_vocal(inst, vocal, phase) # nopep8
text_widget.write(base_text + 'Done!\n') # nopep8 Write Command Text
update_progress(**progress_kwargs,
step=0.7)
# Save Files
text_widget.write(base_text + 'Saving Files...\n') # nopep8 Write Command Text
save_files(wav_instrument, wav_vocals)
text_widget.write(base_text + 'Done!\n') # nopep8 Write Command Text
update_progress(**progress_kwargs,
step=0.8)
else:
# Save Output Image (Mask)
if data['output_image']:
text_widget.write(base_text + 'Creating Mask...\n') # nopep8 Write Command Text
output_image()
text_widget.write(base_text + 'Done!\n') # nopep8 Write Command Text
text_widget.write(base_text + 'Completed Seperation!\n\n') # nopep8 Write Command Text
except Exception as e:
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
message = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\nFile: {music_file}\nLoop: {loop_num}\nPlease contact the creator and attach a screenshot of this error with the file and settings that caused it!'
tk.messagebox.showerror(master=window,
title='Untracked Error',
message=message)
print(traceback_text)
print(type(e).__name__, e)
print(message)
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
os.remove('temp.wav')
progress_var.set(0) # Update Progress
text_widget.write(f'Conversion(s) Completed and Saving all Files!\n') # nopep8 Write Command Text
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') # nopep8
button_widget.configure(state=tk.NORMAL) # Enable Button

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import pprint
import argparse
import os
import cv2
import librosa
import numpy as np
import soundfile as sf
from tqdm import tqdm
from lib_v4 import dataset
from lib_v4 import nets
from lib_v4 import spec_utils
import torch
# 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._load_models()
# self.offset = model.offset
def _load_models(self):
self.text_widget.write('Loading models...\n') # nopep8 Write Command Text
# -Instrumental-
if os.path.isfile(data['instrumentalModel']):
device = torch.device('cpu')
model = nets.CascadedASPPNet(self.data['n_fft'])
model.load_state_dict(torch.load(self.data['instrumentalModel'],
map_location=device))
if torch.cuda.is_available() and self.data['gpu'] >= 0:
device = torch.device('cuda:{}'.format(self.data['gpu']))
model.to(device)
self.models['instrumental'] = model
self.devices['instrumental'] = device
# -Vocal-
elif os.path.isfile(data['vocalModel']):
device = torch.device('cpu')
model = nets.CascadedASPPNet(self.data['n_fft'])
model.load_state_dict(torch.load(self.data['vocalModel'],
map_location=device))
if torch.cuda.is_available() and self.data['gpu'] >= 0:
device = torch.device('cuda:{}'.format(self.data['gpu']))
model.to(device)
self.models['vocal'] = model
self.devices['vocal'] = device
# -Stack-
if os.path.isfile(self.data['stackModel']):
device = torch.device('cpu')
model = nets.CascadedASPPNet(self.data['n_fft'])
model.load_state_dict(torch.load(self.data['stackModel'],
map_location=device))
if torch.cuda.is_available() and self.data['gpu'] >= 0:
device = torch.device('cuda:{}'.format(self.data['gpu']))
model.to(device)
self.models['stack'] = model
self.devices['stack'] = device
self.text_widget.write('Done!\n')
def _execute(self, X_mag_pad, roi_size, n_window, device, model):
model.eval()
with torch.no_grad():
preds = []
for i in tqdm(range(n_window)):
start = i * roi_size
X_mag_window = X_mag_pad[None, :, :,
start:start + self.data['window_size']]
X_mag_window = torch.from_numpy(X_mag_window).to(device)
pred = model.predict(X_mag_window)
pred = pred.detach().cpu().numpy()
preds.append(pred[0])
pred = np.concatenate(preds, axis=2)
return pred
def preprocess(self, X_spec):
X_mag = np.abs(X_spec)
X_phase = np.angle(X_spec)
return X_mag, X_phase
def inference(self, X_spec, device, model):
X_mag, X_phase = self.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,
self.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 = self._execute(X_mag_pad, roi_size, n_window,
device, model)
pred = pred[:, :, :n_frame]
return pred * coef, X_mag, np.exp(1.j * X_phase)
def inference_tta(self, X_spec, device, model):
X_mag, X_phase = self.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,
self.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 = self._execute(X_mag_pad, roi_size, n_window,
device, model)
pred = pred[:, :, :n_frame]
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 = self._execute(X_mag_pad, roi_size, n_window,
device, model)
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)
data = {
# Paths
'input_paths': None,
'export_path': None,
# Processing Options
'gpu': -1,
'postprocess': True,
'tta': True,
'output_image': True,
# Models
'instrumentalModel': None,
'vocalModel': None,
'stackModel': None,
'useModel': None,
# Stack Options
'stackPasses': 0,
'stackOnly': False,
'saveAllStacked': False,
# Constants
'sr': 44_100,
'hop_length': 1_024,
'window_size': 512,
'n_fft': 2_048,
}
default_sr = data['sr']
default_hop_length = data['hop_length']
default_window_size = data['window_size']
default_n_fft = data['n_fft']
def update_progress(progress_var, total_files, total_loops, file_num, loop_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 / total_loops) * (loop_num + step)
progress_var.set(progress)
def get_baseText(total_files, total_loops, file_num, loop_num):
"""Create the base text for the command widget"""
text = 'File {file_num}/{total_files}:{loop} '.format(file_num=file_num,
total_files=total_files,
loop='' if total_loops <= 1 else f' ({loop_num+1}/{total_loops})')
return text
def update_constants(model_name):
"""
Decode the conversion settings from the model's name
"""
global data
text = model_name.replace('.pth', '')
text_parts = text.split('_')[1:]
data['sr'] = default_sr
data['hop_length'] = default_hop_length
data['window_size'] = default_window_size
data['n_fft'] = default_n_fft
for text_part in text_parts:
if 'sr' in text_part:
text_part = text_part.replace('sr', '')
if text_part.isdecimal():
try:
data['sr'] = int(text_part)
continue
except ValueError:
# Cannot convert string to int
pass
if 'hl' in text_part:
text_part = text_part.replace('hl', '')
if text_part.isdecimal():
try:
data['hop_length'] = int(text_part)
continue
except ValueError:
# Cannot convert string to int
pass
if 'w' in text_part:
text_part = text_part.replace('w', '')
if text_part.isdecimal():
try:
data['window_size'] = int(text_part)
continue
except ValueError:
# Cannot convert string to int
pass
if 'nf' in text_part:
text_part = text_part.replace('nf', '')
if text_part.isdecimal():
try:
data['n_fft'] = int(text_part)
continue
except ValueError:
# Cannot convert string to int
pass
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] + '-'
# -Vocal-
elif os.path.isfile(data['vocalModel']):
modelFolderName += os.path.splitext(os.path.basename(data['vocalModel']))[0] + '-'
# -Stack-
if os.path.isfile(data['stackModel']):
modelFolderName += os.path.splitext(os.path.basename(data['stackModel']))[0]
else:
modelFolderName = modelFolderName[:-1]
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):
def save_files(wav_instrument, wav_vocals):
"""Save output music files"""
vocal_name = None
instrumental_name = None
folder = ''
# Get the Suffix Name
if (not loop_num or
loop_num == (total_loops - 1)): # First or Last Loop
if data['stackOnly']:
if loop_num == (total_loops - 1): # Last Loop
if not (total_loops - 1): # Only 1 Loop
vocal_name = '(Vocals)'
instrumental_name = '(Instrumental)'
else:
vocal_name = '(Vocal_Final_Stacked_Output)'
instrumental_name = '(Instrumental_Final_Stacked_Output)'
elif data['useModel'] == 'instrumental':
if not loop_num: # First Loop
vocal_name = '(Vocals)'
if loop_num == (total_loops - 1): # Last Loop
if not (total_loops - 1): # Only 1 Loop
instrumental_name = '(Instrumental)'
else:
instrumental_name = '(Instrumental_Final_Stacked_Output)'
elif data['useModel'] == 'vocal':
if not loop_num: # First Loop
instrumental_name = '(Instrumental)'
if loop_num == (total_loops - 1): # Last Loop
if not (total_loops - 1): # Only 1 Loop
vocal_name = '(Vocals)'
else:
vocal_name = '(Vocals_Final_Stacked_Output)'
if data['useModel'] == 'vocal':
# Reverse names
vocal_name, instrumental_name = instrumental_name, vocal_name
elif data['saveAllStacked']:
folder = os.path.splitext(os.path.basename(base_name))[0] + ' Stacked Outputs' # nopep8
folder = os.path.basename(folder) + '/'
folder_path = os.path.dirname(base_name)
folder_path = os.path.join(folder_path, folder)
if not os.path.isdir(folder_path):
os.mkdir(folder_path)
if data['stackOnly']:
vocal_name = f'(Vocal_{loop_num}_Stacked_Output)'
instrumental_name = f'(Instrumental_{loop_num}_Stacked_Output)'
elif (data['useModel'] == 'vocal' or
data['useModel'] == 'instrumental'):
vocal_name = f'(Vocals_{loop_num}_Stacked_Output)'
instrumental_name = f'(Instrumental_{loop_num}_Stacked_Output)'
if data['useModel'] == 'vocal':
# 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.T, sr)
appendModelFolderName = modelFolderName.replace('/', '_')
# -Save files-
# Instrumental
if instrumental_name is not None:
instrumental_path = '{base_path}/{folder}{file_name}.wav'.format(
base_path=os.path.dirname(base_name),
folder=folder,
file_name=f'{os.path.basename(base_name)}_{instrumental_name}{appendModelFolderName}',
)
sf.write(instrumental_path,
wav_instrument.T, sr)
# Vocal
if vocal_name is not None:
vocal_path = '{base_path}/{folder}{file_name}.wav'.format(
base_path=os.path.dirname(base_name),
folder=folder,
file_name=f'{os.path.basename(base_name)}_{vocal_name}{appendModelFolderName}',
)
sf.write(vocal_path,
wav_vocals.T, sr)
data.update(kwargs)
# Update default settings
global default_sr
global default_hop_length
global default_window_size
global default_n_fft
default_sr = data['sr']
default_hop_length = data['hop_length']
default_window_size = data['window_size']
default_n_fft = data['n_fft']
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()
if modelFolderName:
folder_path = f'{data["export_path"]}{modelFolderName}'
if not os.path.isdir(folder_path):
os.mkdir(folder_path)
# Determine Loops
total_loops = data['stackPasses']
if not data['stackOnly']:
total_loops += 1
for file_num, music_file in enumerate(data['input_paths'], start=1):
try:
# Determine File Name
base_name = f'{data["export_path"]}{modelFolderName}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
# --Seperate Music Files--
for loop_num in range(total_loops):
# -Determine which model will be used-
if not loop_num:
# First Iteration
if data['stackOnly']:
if os.path.isfile(data['stackModel']):
model_name = os.path.basename(data['stackModel'])
model = vocal_remover.models['stack']
device = vocal_remover.devices['stack']
else:
raise ValueError(f'Selected stack only model, however, stack model path file cannot be found\nPath: "{data["stackModel"]}"') # nopep8
else:
model_name = os.path.basename(data[f'{data["useModel"]}Model'])
model = vocal_remover.models[data['useModel']]
device = vocal_remover.devices[data['useModel']]
else:
model_name = os.path.basename(data['stackModel'])
# Every other iteration
model = vocal_remover.models['stack']
device = vocal_remover.devices['stack']
# Reference new music file
music_file = 'temp.wav'
# -Get text and update progress-
base_text = get_baseText(total_files=len(data['input_paths']),
total_loops=total_loops,
file_num=file_num,
loop_num=loop_num)
progress_kwargs = {'progress_var': progress_var,
'total_files': len(data['input_paths']),
'total_loops': total_loops,
'file_num': file_num,
'loop_num': loop_num}
update_progress(**progress_kwargs,
step=0)
update_constants(model_name)
# -Go through the different steps of seperation-
# Wave source
text_widget.write(base_text + 'Loading wave source...\n')
X, sr = librosa.load(music_file, data['sr'], False,
dtype=np.float32, res_type='kaiser_fast')
if X.ndim == 1:
X = np.asarray([X, X])
text_widget.write(base_text + 'Done!\n')
update_progress(**progress_kwargs,
step=0.1)
# Stft of wave source
text_widget.write(base_text + 'Stft of wave source...\n')
X = spec_utils.wave_to_spectrogram(X,
data['hop_length'], data['n_fft'])
if data['tta']:
pred, X_mag, X_phase = vocal_remover.inference_tta(X,
device=device,
model=model)
else:
pred, X_mag, X_phase = vocal_remover.inference(X,
device=device,
model=model)
text_widget.write(base_text + 'Done!\n')
update_progress(**progress_kwargs,
step=0.6)
# Postprocess
if data['postprocess']:
text_widget.write(base_text + 'Post processing...\n')
pred_inv = np.clip(X_mag - pred, 0, np.inf)
pred = spec_utils.mask_silence(pred, pred_inv)
text_widget.write(base_text + 'Done!\n')
update_progress(**progress_kwargs,
step=0.65)
# Inverse stft
text_widget.write(base_text + 'Inverse stft of instruments and vocals...\n') # nopep8
y_spec = pred * X_phase
wav_instrument = spec_utils.spectrogram_to_wave(y_spec,
hop_length=data['hop_length'])
v_spec = np.clip(X_mag - pred, 0, np.inf) * X_phase
wav_vocals = spec_utils.spectrogram_to_wave(v_spec,
hop_length=data['hop_length'])
text_widget.write(base_text + 'Done!\n')
update_progress(**progress_kwargs,
step=0.7)
# Save output music files
text_widget.write(base_text + 'Saving Files...\n')
save_files(wav_instrument, wav_vocals)
text_widget.write(base_text + 'Done!\n')
update_progress(**progress_kwargs,
step=0.8)
else:
# 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)
_, 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)
_, 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}"\nFile: {music_file}\nLoop: {loop_num}\nPlease contact the creator and attach a screenshot of this error with the file and settings that caused it!'
tk.messagebox.showerror(master=window,
title='Untracked Error',
message=message)
print(traceback_text)
print(type(e).__name__, e)
print(message)
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
os.remove('temp.wav')
progress_var.set(0)
text_widget.write(f'Conversion(s) Completed and Saving all Files!\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') # nopep8
button_widget.configure(state=tk.NORMAL) # Enable Button