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Delete inference_v5_ensemble.py
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from functools import total_ordering
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import pprint
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import argparse
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import os
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from statistics import mode
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import cv2
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import librosa
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import numpy as np
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import soundfile as sf
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import shutil
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from tqdm import tqdm
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from lib_v5 import dataset
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from lib_v5 import spec_utils
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from lib_v5.model_param_init import ModelParameters
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import torch
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# Command line text parsing and widget manipulation
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from collections import defaultdict
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import tkinter as tk
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import traceback # Error Message Recent Calls
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import time # Timer
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class VocalRemover(object):
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def __init__(self, data, text_widget: tk.Text):
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self.data = data
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self.text_widget = text_widget
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# self.offset = model.offset
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data = {
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# Paths
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'input_paths': None,
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'export_path': None,
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# Processing Options
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'gpu': -1,
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'postprocess': True,
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'tta': True,
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'save': True,
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'output_image': True,
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# Models
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'instrumentalModel': None,
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'useModel': None,
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# Constants
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'window_size': 512,
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'agg': 10,
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'ensChoose': 'HP1 Models'
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}
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default_window_size = data['window_size']
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default_agg = data['agg']
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def update_progress(progress_var, total_files, file_num, step: float = 1):
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"""Calculate the progress for the progress widget in the GUI"""
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base = (100 / total_files)
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progress = base * (file_num - 1)
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progress += base * step
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progress_var.set(progress)
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def get_baseText(total_files, file_num):
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"""Create the base text for the command widget"""
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text = 'File {file_num}/{total_files} '.format(file_num=file_num,
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total_files=total_files)
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return text
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def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress_var: tk.Variable,
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**kwargs: dict):
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global args
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global nn_arch_sizes
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nn_arch_sizes = [
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31191, # default
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33966, 123821, 123812, 537238 # custom
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]
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p = argparse.ArgumentParser()
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p.add_argument('--aggressiveness',type=float, default=data['agg']/100)
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p.add_argument('--high_end_process', type=str, default='mirroring')
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args = p.parse_args()
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def save_files(wav_instrument, wav_vocals):
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"""Save output music files"""
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vocal_name = '(Vocals)'
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instrumental_name = '(Instrumental)'
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save_path = os.path.dirname(base_name)
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# Swap names if vocal model
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VModel="Vocal"
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if VModel in model_name:
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# Reverse names
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vocal_name, instrumental_name = instrumental_name, vocal_name
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# Save Temp File
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# For instrumental the instrumental is the temp file
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# and for vocal the instrumental is the temp file due
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# to reversement
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sf.write(f'temp.wav',
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wav_instrument, mp.param['sr'])
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# -Save files-
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# Instrumental
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if instrumental_name is not None:
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instrumental_path = '{save_path}/{file_name}.wav'.format(
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save_path=save_path,
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file_name = f'{os.path.basename(base_name)}_{ModelName_1}_{instrumental_name}',
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)
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sf.write(instrumental_path,
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wav_instrument, mp.param['sr'])
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# Vocal
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if vocal_name is not None:
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vocal_path = '{save_path}/{file_name}.wav'.format(
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save_path=save_path,
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file_name=f'{os.path.basename(base_name)}_{ModelName_1}_{vocal_name}',
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)
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sf.write(vocal_path,
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wav_vocals, mp.param['sr'])
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data.update(kwargs)
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# Update default settings
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global default_window_size
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global default_agg
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default_window_size = data['window_size']
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default_agg = data['agg']
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stime = time.perf_counter()
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progress_var.set(0)
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text_widget.clear()
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button_widget.configure(state=tk.DISABLED) # Disable Button
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# Separation Preperation
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try: #Ensemble Dictionary
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HP1_Models = [
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{
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'model_name':'HP_4BAND_44100_A',
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'model_params':'lib_v5/modelparams/4band_44100.json',
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'model_location':'models/Main Models/HP_4BAND_44100_A.pth',
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'using_archtecture': '123821KB',
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'loop_name': 'Ensemble Mode - Model 1/2'
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},
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{
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'model_name':'HP_4BAND_44100_B',
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'model_params':'lib_v5/modelparams/4band_v2.json',
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'model_location':'models/Main Models/HP_4BAND_44100_B.pth',
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'using_archtecture': '123821KB',
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'loop_name': 'Ensemble Mode - Model 2/2'
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}
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]
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HP2_Models = [
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{
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'model_name':'HP2_4BAND_44100_1',
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'model_params':'lib_v5/modelparams/4band_44100.json',
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'model_location':'models/Main Models/HP2_4BAND_44100_1.pth',
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'using_archtecture': '537238KB',
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'loop_name': 'Ensemble Mode - Model 1/3'
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},
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{
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'model_name':'HP2_4BAND_44100_2',
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'model_params':'lib_v5/modelparams/4band_44100.json',
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'model_location':'models/Main Models/HP2_4BAND_44100_2.pth',
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'using_archtecture': '537238KB',
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'loop_name': 'Ensemble Mode - Model 2/3'
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},
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{
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'model_name':'HP2_3BAND_44100_MSB2',
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'model_params':'lib_v5/modelparams/3band_44100_msb2.json',
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'model_location':'models/Main Models/HP2_3BAND_44100_MSB2.pth',
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'using_archtecture': '537238KB',
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'loop_name': 'Ensemble Mode - Model 3/3'
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}
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]
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All_HP_Models = [
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{
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'model_name':'HP_4BAND_44100_A',
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'model_params':'lib_v5/modelparams/4band_44100.json',
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'model_location':'models/Main Models/HP_4BAND_44100_A.pth',
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'using_archtecture': '123821KB',
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'loop_name': 'Ensemble Mode - Model 1/5'
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},
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{
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'model_name':'HP_4BAND_44100_B',
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'model_params':'lib_v5/modelparams/4band_v2.json',
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'model_location':'models/Main Models/HP_4BAND_44100_B.pth',
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'using_archtecture': '123821KB',
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'loop_name': 'Ensemble Mode - Model 2/5'
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},
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{
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'model_name':'HP2_4BAND_44100_1',
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'model_params':'lib_v5/modelparams/4band_44100.json',
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'model_location':'models/Main Models/HP2_4BAND_44100_1.pth',
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'using_archtecture': '537238KB',
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'loop_name': 'Ensemble Mode - Model 3/5'
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},
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{
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'model_name':'HP2_4BAND_44100_2',
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'model_params':'lib_v5/modelparams/4band_44100.json',
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'model_location':'models/Main Models/HP2_4BAND_44100_2.pth',
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'using_archtecture': '537238KB',
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'loop_name': 'Ensemble Mode - Model 4/5'
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},
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{
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'model_name':'HP2_3BAND_44100_MSB2',
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'model_params':'lib_v5/modelparams/3band_44100_msb2.json',
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'model_location':'models/Main Models/HP2_3BAND_44100_MSB2.pth',
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'using_archtecture': '537238KB',
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'loop_name': 'Ensemble Mode - Model 5/5'
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}
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]
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Vocal_Models = [
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{
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'model_name':'HP_Vocal_4BAND_44100',
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'model_params':'lib_v5/modelparams/4band_44100.json',
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'model_location':'models/Main Models/HP_Vocal_4BAND_44100.pth',
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'using_archtecture': '123821KB',
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'loop_name': 'Ensemble Mode - Model 1/2'
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},
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{
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'model_name':'HP_Vocal_AGG_4BAND_44100',
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'model_params':'lib_v5/modelparams/4band_44100.json',
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'model_location':'models/Main Models/HP_Vocal_AGG_4BAND_44100.pth',
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'using_archtecture': '123821KB',
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'loop_name': 'Ensemble Mode - Model 2/2'
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}
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]
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if data['ensChoose'] == 'HP1 Models':
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loops = HP1_Models
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ensefolder = 'HP_Models_Saved_Outputs'
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ensemode = 'HP_Models'
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if data['ensChoose'] == 'HP2 Models':
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loops = HP2_Models
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ensefolder = 'HP2_Models_Saved_Outputs'
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ensemode = 'HP2_Models'
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if data['ensChoose'] == 'All HP Models':
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loops = All_HP_Models
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ensefolder = 'All_HP_Models_Saved_Outputs'
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ensemode = 'All_HP_Models'
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if data['ensChoose'] == 'Vocal Models':
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loops = Vocal_Models
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ensefolder = 'Vocal_Models_Saved_Outputs'
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ensemode = 'Vocal_Models'
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#Prepare Audiofile(s)
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for file_num, music_file in enumerate(data['input_paths'], start=1):
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# -Get text and update progress-
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base_text = get_baseText(total_files=len(data['input_paths']),
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file_num=file_num)
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progress_kwargs = {'progress_var': progress_var,
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'total_files': len(data['input_paths']),
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'file_num': file_num}
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update_progress(**progress_kwargs,
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step=0)
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#Prepare to loop models
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for i, c in tqdm(enumerate(loops), disable=True, desc='Iterations..'):
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text_widget.write(c['loop_name'] + '\n\n')
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text_widget.write(base_text + 'Loading ' + c['model_name'] + '... ')
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arch_now = c['using_archtecture']
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if arch_now == '123821KB':
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from lib_v5 import nets_123821KB as nets
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elif arch_now == '537238KB':
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from lib_v5 import nets_537238KB as nets
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elif arch_now == '537227KB':
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from lib_v5 import nets_537227KB as nets
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def determineenseFolderName():
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"""
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Determine the name that is used for the folder and appended
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to the back of the music files
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"""
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enseFolderName = ''
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if str(ensefolder):
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enseFolderName += os.path.splitext(os.path.basename(ensefolder))[0]
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if enseFolderName:
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enseFolderName = '/' + enseFolderName
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return enseFolderName
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enseFolderName = determineenseFolderName()
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if enseFolderName:
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folder_path = f'{data["export_path"]}{enseFolderName}'
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if not os.path.isdir(folder_path):
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os.mkdir(folder_path)
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# Determine File Name
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base_name = f'{data["export_path"]}{enseFolderName}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
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enseExport = f'{data["export_path"]}{enseFolderName}/'
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trackname = f'{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
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ModelName_1=(c['model_name'])
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print('Model Parameters:', c['model_params'])
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mp = ModelParameters(c['model_params'])
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#Load model
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if os.path.isfile(c['model_location']):
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device = torch.device('cpu')
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model = nets.CascadedASPPNet(mp.param['bins'] * 2)
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model.load_state_dict(torch.load(c['model_location'],
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map_location=device))
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if torch.cuda.is_available() and data['gpu'] >= 0:
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device = torch.device('cuda:{}'.format(data['gpu']))
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model.to(device)
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text_widget.write('Done!\n')
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model_name = os.path.basename(c["model_name"])
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# -Go through the different steps of seperation-
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# Wave source
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text_widget.write(base_text + 'Loading wave source... ')
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X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
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bands_n = len(mp.param['band'])
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for d in range(bands_n, 0, -1):
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bp = mp.param['band'][d]
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if d == bands_n: # high-end band
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X_wave[d], _ = librosa.load(
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music_file, bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
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if X_wave[d].ndim == 1:
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X_wave[d] = np.asarray([X_wave[d], X_wave[d]])
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else: # lower bands
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X_wave[d] = librosa.resample(X_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
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# Stft of wave source
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X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'],
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mp.param['mid_side_b2'], mp.param['reverse'])
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if d == bands_n and args.high_end_process != 'none':
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input_high_end_h = (bp['n_fft']//2 - bp['crop_stop']) + (mp.param['pre_filter_stop'] - mp.param['pre_filter_start'])
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input_high_end = X_spec_s[d][:, bp['n_fft']//2-input_high_end_h:bp['n_fft']//2, :]
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text_widget.write('Done!\n')
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update_progress(**progress_kwargs,
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step=0.1)
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text_widget.write(base_text + 'Stft of wave source... ')
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text_widget.write('Done!\n')
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text_widget.write(base_text + "Please Wait...\n")
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X_spec_m = spec_utils.combine_spectrograms(X_spec_s, mp)
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del X_wave, X_spec_s
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def inference(X_spec, device, model, aggressiveness):
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def _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness):
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model.eval()
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with torch.no_grad():
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preds = []
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iterations = [n_window]
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total_iterations = sum(iterations)
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text_widget.write(base_text + "Processing "f"{total_iterations} Slices... ")
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for i in tqdm(range(n_window)):
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update_progress(**progress_kwargs,
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step=(0.1 + (0.8/n_window * i)))
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start = i * roi_size
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X_mag_window = X_mag_pad[None, :, :, start:start + data['window_size']]
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X_mag_window = torch.from_numpy(X_mag_window).to(device)
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pred = model.predict(X_mag_window, aggressiveness)
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pred = pred.detach().cpu().numpy()
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preds.append(pred[0])
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pred = np.concatenate(preds, axis=2)
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text_widget.write('Done!\n')
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return pred
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def preprocess(X_spec):
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X_mag = np.abs(X_spec)
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X_phase = np.angle(X_spec)
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return X_mag, X_phase
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X_mag, X_phase = preprocess(X_spec)
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coef = X_mag.max()
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X_mag_pre = X_mag / coef
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n_frame = X_mag_pre.shape[2]
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pad_l, pad_r, roi_size = dataset.make_padding(n_frame,
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data['window_size'], model.offset)
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n_window = int(np.ceil(n_frame / roi_size))
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X_mag_pad = np.pad(
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X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
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pred = _execute(X_mag_pad, roi_size, n_window,
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device, model, aggressiveness)
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pred = pred[:, :, :n_frame]
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if data['tta']:
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pad_l += roi_size // 2
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pad_r += roi_size // 2
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n_window += 1
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X_mag_pad = np.pad(
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X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
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pred_tta = _execute(X_mag_pad, roi_size, n_window,
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device, model, aggressiveness)
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pred_tta = pred_tta[:, :, roi_size // 2:]
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pred_tta = pred_tta[:, :, :n_frame]
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return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.j * X_phase)
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else:
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return pred * coef, X_mag, np.exp(1.j * X_phase)
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aggressiveness = {'value': args.aggressiveness, 'split_bin': mp.param['band'][1]['crop_stop']}
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if data['tta']:
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text_widget.write(base_text + "Running Inferences (TTA)... \n")
|
||||
else:
|
||||
text_widget.write(base_text + "Running Inference... \n")
|
||||
|
||||
pred, X_mag, X_phase = inference(X_spec_m,
|
||||
device,
|
||||
model, aggressiveness)
|
||||
|
||||
update_progress(**progress_kwargs,
|
||||
step=0.85)
|
||||
|
||||
# Postprocess
|
||||
if data['postprocess']:
|
||||
text_widget.write(base_text + 'Post processing... ')
|
||||
pred_inv = np.clip(X_mag - pred, 0, np.inf)
|
||||
pred = spec_utils.mask_silence(pred, pred_inv)
|
||||
text_widget.write('Done!\n')
|
||||
|
||||
update_progress(**progress_kwargs,
|
||||
step=0.85)
|
||||
|
||||
# Inverse stft
|
||||
text_widget.write(base_text + 'Inverse stft of instruments and vocals... ') # nopep8
|
||||
y_spec_m = pred * X_phase
|
||||
v_spec_m = X_spec_m - y_spec_m
|
||||
|
||||
if args.high_end_process.startswith('mirroring'):
|
||||
input_high_end_ = spec_utils.mirroring(args.high_end_process, y_spec_m, input_high_end, mp)
|
||||
wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp, input_high_end_h, input_high_end_)
|
||||
else:
|
||||
wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp)
|
||||
|
||||
if args.high_end_process.startswith('mirroring'):
|
||||
input_high_end_ = spec_utils.mirroring(args.high_end_process, v_spec_m, input_high_end, mp)
|
||||
|
||||
wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp, input_high_end_h, input_high_end_)
|
||||
else:
|
||||
wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp)
|
||||
|
||||
text_widget.write('Done!\n')
|
||||
|
||||
update_progress(**progress_kwargs,
|
||||
step=0.9)
|
||||
|
||||
# Save output music files
|
||||
text_widget.write(base_text + 'Saving Files... ')
|
||||
save_files(wav_instrument, wav_vocals)
|
||||
text_widget.write('Done!\n')
|
||||
|
||||
# Save output image
|
||||
if data['output_image']:
|
||||
with open('{}_Instruments.jpg'.format(base_name), mode='wb') as f:
|
||||
image = spec_utils.spectrogram_to_image(y_spec_m)
|
||||
_, bin_image = cv2.imencode('.jpg', image)
|
||||
bin_image.tofile(f)
|
||||
with open('{}_Vocals.jpg'.format(base_name), mode='wb') as f:
|
||||
image = spec_utils.spectrogram_to_image(v_spec_m)
|
||||
_, bin_image = cv2.imencode('.jpg', image)
|
||||
bin_image.tofile(f)
|
||||
|
||||
text_widget.write(base_text + 'Clearing CUDA Cache... ')
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
time.sleep(3)
|
||||
|
||||
text_widget.write('Done!\n')
|
||||
|
||||
text_widget.write(base_text + 'Completed Seperation!\n\n')
|
||||
|
||||
# Emsembling Outputs
|
||||
def get_files(folder="", prefix="", suffix=""):
|
||||
return [f"{folder}{i}" for i in os.listdir(folder) if i.startswith(prefix) if i.endswith(suffix)]
|
||||
|
||||
ensambles = [
|
||||
{
|
||||
'algorithm':'min_mag',
|
||||
'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json',
|
||||
'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Instrumental).wav"),
|
||||
'output':'{}_Ensembled_{}_(Instrumental)'.format(trackname, ensemode),
|
||||
'type': 'Instrumentals'
|
||||
},
|
||||
{
|
||||
'algorithm':'max_mag',
|
||||
'model_params':'lib_v5/modelparams/1band_sr44100_hl512.json',
|
||||
'files':get_files(folder=enseExport, prefix=trackname, suffix="_(Vocals).wav"),
|
||||
'output': '{}_Ensembled_{}_(Vocals)'.format(trackname, ensemode),
|
||||
'type': 'Vocals'
|
||||
}
|
||||
]
|
||||
|
||||
for i, e in tqdm(enumerate(ensambles), desc="Ensembling..."):
|
||||
|
||||
text_widget.write(base_text + "Ensembling " + e['type'] + "... ")
|
||||
|
||||
wave, specs = {}, {}
|
||||
|
||||
mp = ModelParameters(e['model_params'])
|
||||
|
||||
for i in range(len(e['files'])):
|
||||
spec = {}
|
||||
|
||||
for d in range(len(mp.param['band']), 0, -1):
|
||||
bp = mp.param['band'][d]
|
||||
|
||||
if d == len(mp.param['band']): # high-end band
|
||||
wave[d], _ = librosa.load(
|
||||
e['files'][i], bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
|
||||
|
||||
if len(wave[d].shape) == 1: # mono to stereo
|
||||
wave[d] = np.array([wave[d], wave[d]])
|
||||
else: # lower bands
|
||||
wave[d] = librosa.resample(wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
|
||||
|
||||
spec[d] = spec_utils.wave_to_spectrogram(wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse'])
|
||||
|
||||
specs[i] = spec_utils.combine_spectrograms(spec, mp)
|
||||
|
||||
del wave
|
||||
|
||||
sf.write(os.path.join('{}'.format(data['export_path']),'{}.wav'.format(e['output'])),
|
||||
spec_utils.cmb_spectrogram_to_wave(spec_utils.ensembling(e['algorithm'],
|
||||
specs), mp), mp.param['sr'])
|
||||
|
||||
if not data['save']: # Deletes all outputs if Save All Outputs: is checked
|
||||
files = e['files']
|
||||
for file in files:
|
||||
os.remove(file)
|
||||
|
||||
text_widget.write("Done!\n")
|
||||
|
||||
update_progress(**progress_kwargs,
|
||||
step=0.95)
|
||||
text_widget.write("\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}\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
|
||||
|
||||
if len(os.listdir(enseExport)) == 0: #Check if the folder is empty
|
||||
shutil.rmtree(folder_path) #Delete folder if empty
|
||||
|
||||
update_progress(**progress_kwargs,
|
||||
step=1)
|
||||
|
||||
print('Done!')
|
||||
|
||||
os.remove('temp.wav')
|
||||
|
||||
progress_var.set(0)
|
||||
text_widget.write(f'Conversions Completed!\n')
|
||||
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') # nopep8
|
||||
torch.cuda.empty_cache()
|
||||
button_widget.configure(state=tk.NORMAL) #Enable Button
|
||||
Loading…
Reference in New Issue
Block a user