Upload separate.py

separate.py

@@ -0,0 +1,1459 @@
+from __future__ import annotations
+from typing import TYPE_CHECKING
+from demucs.apply import apply_model, demucs_segments
+from demucs.hdemucs import HDemucs
+from demucs.model_v2 import auto_load_demucs_model_v2
+from demucs.pretrained import get_model as _gm
+from demucs.utils import apply_model_v1
+from demucs.utils import apply_model_v2
+from lib_v5.tfc_tdf_v3 import TFC_TDF_net, STFT
+from lib_v5 import spec_utils
+from lib_v5.vr_network import nets
+from lib_v5.vr_network import nets_new
+from lib_v5.vr_network.model_param_init import ModelParameters
+from pathlib import Path
+from gui_data.constants import *
+from gui_data.error_handling import *
+from scipy import signal
+import audioread
+import gzip
+import librosa
+import math
+import numpy as np
+import onnxruntime as ort
+import os
+import torch
+import warnings
+import pydub
+import soundfile as sf
+import lib_v5.mdxnet as MdxnetSet
+import math
+#import random
+from onnx import load
+from onnx2pytorch import ConvertModel
+import gc
+ 
+if TYPE_CHECKING:
+    from UVR import ModelData
+
+# if not is_macos:
+#     import torch_directml
+
+mps_available = torch.backends.mps.is_available() if is_macos else False
+cuda_available = torch.cuda.is_available()
+
+# def get_gpu_info():
+#     directml_device, directml_available = DIRECTML_DEVICE, False
+    
+#     if not is_macos:
+#         directml_available = torch_directml.is_available()
+
+#         if directml_available:
+#             directml_device = str(torch_directml.device()).partition(":")[0]
+
+#     return directml_device, directml_available
+
+# DIRECTML_DEVICE, directml_available = get_gpu_info()
+
+def clear_gpu_cache():
+    gc.collect()
+    if is_macos:
+        torch.mps.empty_cache()
+    else:
+        torch.cuda.empty_cache()
+
+warnings.filterwarnings("ignore")
+cpu = torch.device('cpu')
+
+class SeperateAttributes:
+    def __init__(self, model_data: ModelData, 
+                 process_data: dict, 
+                 main_model_primary_stem_4_stem=None, 
+                 main_process_method=None, 
+                 is_return_dual=True, 
+                 main_model_primary=None, 
+                 vocal_stem_path=None, 
+                 master_inst_source=None,
+                 master_vocal_source=None):
+        
+        self.list_all_models: list
+        self.process_data = process_data
+        self.progress_value = 0
+        self.set_progress_bar = process_data['set_progress_bar']
+        self.write_to_console = process_data['write_to_console']
+        if vocal_stem_path:
+            self.audio_file, self.audio_file_base = vocal_stem_path
+            self.audio_file_base_voc_split = lambda stem, split:os.path.join(self.export_path, f'{self.audio_file_base.replace("_(Vocals)", "")}_({stem}_{split}).wav')
+        else:
+            self.audio_file = process_data['audio_file']
+            self.audio_file_base = process_data['audio_file_base']
+            self.audio_file_base_voc_split = None
+        self.export_path = process_data['export_path']
+        self.cached_source_callback = process_data['cached_source_callback']
+        self.cached_model_source_holder = process_data['cached_model_source_holder']
+        self.is_4_stem_ensemble = process_data['is_4_stem_ensemble']
+        self.list_all_models = process_data['list_all_models']
+        self.process_iteration = process_data['process_iteration']
+        self.is_return_dual = is_return_dual
+        self.is_pitch_change = model_data.is_pitch_change
+        self.semitone_shift = model_data.semitone_shift
+        self.is_match_frequency_pitch = model_data.is_match_frequency_pitch
+        self.overlap = model_data.overlap
+        self.overlap_mdx = model_data.overlap_mdx
+        self.overlap_mdx23 = model_data.overlap_mdx23
+        self.is_mdx_combine_stems = model_data.is_mdx_combine_stems
+        self.is_mdx_c = model_data.is_mdx_c
+        self.mdx_c_configs = model_data.mdx_c_configs
+        self.mdxnet_stem_select = model_data.mdxnet_stem_select
+        self.mixer_path = model_data.mixer_path
+        self.model_samplerate = model_data.model_samplerate
+        self.model_capacity = model_data.model_capacity
+        self.is_vr_51_model = model_data.is_vr_51_model
+        self.is_pre_proc_model = model_data.is_pre_proc_model
+        self.is_secondary_model_activated = model_data.is_secondary_model_activated if not self.is_pre_proc_model else False
+        self.is_secondary_model = model_data.is_secondary_model if not self.is_pre_proc_model else True
+        self.process_method = model_data.process_method
+        self.model_path = model_data.model_path
+        self.model_name = model_data.model_name
+        self.model_basename = model_data.model_basename
+        self.wav_type_set = model_data.wav_type_set
+        self.mp3_bit_set = model_data.mp3_bit_set
+        self.save_format = model_data.save_format
+        self.is_gpu_conversion = model_data.is_gpu_conversion
+        self.is_normalization = model_data.is_normalization
+        self.is_primary_stem_only = model_data.is_primary_stem_only if not self.is_secondary_model else model_data.is_primary_model_primary_stem_only
+        self.is_secondary_stem_only = model_data.is_secondary_stem_only if not self.is_secondary_model else model_data.is_primary_model_secondary_stem_only      
+        self.is_ensemble_mode = model_data.is_ensemble_mode
+        self.secondary_model = model_data.secondary_model #
+        self.primary_model_primary_stem = model_data.primary_model_primary_stem
+        self.primary_stem_native = model_data.primary_stem_native
+        self.primary_stem = model_data.primary_stem #
+        self.secondary_stem = model_data.secondary_stem #
+        self.is_invert_spec = model_data.is_invert_spec #
+        self.is_deverb_vocals = model_data.is_deverb_vocals
+        self.is_mixer_mode = model_data.is_mixer_mode #
+        self.secondary_model_scale = model_data.secondary_model_scale #
+        self.is_demucs_pre_proc_model_inst_mix = model_data.is_demucs_pre_proc_model_inst_mix #
+        self.primary_source_map = {}
+        self.secondary_source_map = {}
+        self.primary_source = None
+        self.secondary_source = None
+        self.secondary_source_primary = None
+        self.secondary_source_secondary = None
+        self.main_model_primary_stem_4_stem = main_model_primary_stem_4_stem
+        self.main_model_primary = main_model_primary
+        self.ensemble_primary_stem = model_data.ensemble_primary_stem
+        self.is_multi_stem_ensemble = model_data.is_multi_stem_ensemble
+        self.is_other_gpu = False
+        self.is_deverb = True
+        self.DENOISER_MODEL = model_data.DENOISER_MODEL
+        self.DEVERBER_MODEL = model_data.DEVERBER_MODEL
+        self.is_source_swap = False
+        self.vocal_split_model = model_data.vocal_split_model
+        self.is_vocal_split_model = model_data.is_vocal_split_model
+        self.master_vocal_path = None
+        self.set_master_inst_source = None
+        self.master_inst_source = master_inst_source
+        self.master_vocal_source = master_vocal_source
+        self.is_save_inst_vocal_splitter = isinstance(master_inst_source, np.ndarray) and model_data.is_save_inst_vocal_splitter
+        self.is_inst_only_voc_splitter = model_data.is_inst_only_voc_splitter
+        self.is_karaoke = model_data.is_karaoke
+        self.is_bv_model = model_data.is_bv_model
+        self.is_bv_model_rebalenced = model_data.bv_model_rebalance and self.is_vocal_split_model
+        self.is_sec_bv_rebalance = model_data.is_sec_bv_rebalance
+        self.stem_path_init = os.path.join(self.export_path, f'{self.audio_file_base}_({self.secondary_stem}).wav')
+        self.deverb_vocal_opt = model_data.deverb_vocal_opt
+        self.is_save_vocal_only = model_data.is_save_vocal_only
+        self.device = cpu
+        self.run_type = ['CPUExecutionProvider']
+        self.is_opencl = False
+        self.device_set = model_data.device_set
+        self.is_use_opencl = model_data.is_use_opencl
+        
+        if self.is_inst_only_voc_splitter or self.is_sec_bv_rebalance:
+            self.is_primary_stem_only = False
+            self.is_secondary_stem_only = False
+        
+        if main_model_primary and self.is_multi_stem_ensemble:
+            self.primary_stem, self.secondary_stem = main_model_primary, secondary_stem(main_model_primary)
+
+        if self.is_gpu_conversion >= 0:
+            if mps_available:
+                self.device, self.is_other_gpu = 'mps', True
+            else:
+                device_prefix = None
+                if self.device_set != DEFAULT:
+                    device_prefix = CUDA_DEVICE#DIRECTML_DEVICE if self.is_use_opencl and directml_available else CUDA_DEVICE
+
+                # if directml_available and self.is_use_opencl:
+                #     self.device = torch_directml.device() if not device_prefix else f'{device_prefix}:{self.device_set}'
+                #     self.is_other_gpu = True
+                if cuda_available:# and not self.is_use_opencl:
+                    self.device = CUDA_DEVICE if not device_prefix else f'{device_prefix}:{self.device_set}'
+                    self.run_type = ['CUDAExecutionProvider']
+
+        if model_data.process_method == MDX_ARCH_TYPE:
+            self.is_mdx_ckpt = model_data.is_mdx_ckpt
+            self.primary_model_name, self.primary_sources = self.cached_source_callback(MDX_ARCH_TYPE, model_name=self.model_basename)
+            self.is_denoise = model_data.is_denoise#
+            self.is_denoise_model = model_data.is_denoise_model#
+            self.is_mdx_c_seg_def = model_data.is_mdx_c_seg_def#
+            self.mdx_batch_size = model_data.mdx_batch_size
+            self.compensate = model_data.compensate
+            self.mdx_segment_size = model_data.mdx_segment_size
+            
+            if self.is_mdx_c:
+                if not self.is_4_stem_ensemble:
+                    self.primary_stem = model_data.ensemble_primary_stem if process_data['is_ensemble_master'] else model_data.primary_stem
+                    self.secondary_stem = model_data.ensemble_secondary_stem if process_data['is_ensemble_master'] else model_data.secondary_stem
+            else:
+                self.dim_f, self.dim_t = model_data.mdx_dim_f_set, 2**model_data.mdx_dim_t_set
+                
+            self.check_label_secondary_stem_runs()
+            self.n_fft = model_data.mdx_n_fft_scale_set
+            self.chunks = model_data.chunks
+            self.margin = model_data.margin
+            self.adjust = 1
+            self.dim_c = 4
+            self.hop = 1024
+
+        if model_data.process_method == DEMUCS_ARCH_TYPE:
+            self.demucs_stems = model_data.demucs_stems if not main_process_method in [MDX_ARCH_TYPE, VR_ARCH_TYPE] else None
+            self.secondary_model_4_stem = model_data.secondary_model_4_stem
+            self.secondary_model_4_stem_scale = model_data.secondary_model_4_stem_scale
+            self.is_chunk_demucs = model_data.is_chunk_demucs
+            self.segment = model_data.segment
+            self.demucs_version = model_data.demucs_version
+            self.demucs_source_list = model_data.demucs_source_list
+            self.demucs_source_map = model_data.demucs_source_map
+            self.is_demucs_combine_stems = model_data.is_demucs_combine_stems
+            self.demucs_stem_count = model_data.demucs_stem_count
+            self.pre_proc_model = model_data.pre_proc_model
+            self.device = cpu if self.is_other_gpu and not self.demucs_version in [DEMUCS_V3, DEMUCS_V4] else self.device
+
+            self.primary_stem = model_data.ensemble_primary_stem if process_data['is_ensemble_master'] else model_data.primary_stem
+            self.secondary_stem = model_data.ensemble_secondary_stem if process_data['is_ensemble_master'] else model_data.secondary_stem
+
+            if (self.is_multi_stem_ensemble or self.is_4_stem_ensemble) and not self.is_secondary_model:
+                self.is_return_dual = False
+            
+            if self.is_multi_stem_ensemble and main_model_primary:
+                self.is_4_stem_ensemble = False
+                if main_model_primary in self.demucs_source_map.keys():
+                    self.primary_stem = main_model_primary
+                    self.secondary_stem = secondary_stem(main_model_primary)
+                elif secondary_stem(main_model_primary) in self.demucs_source_map.keys():
+                    self.primary_stem = secondary_stem(main_model_primary)
+                    self.secondary_stem = main_model_primary
+
+            if self.is_secondary_model and not process_data['is_ensemble_master']:
+                if not self.demucs_stem_count == 2 and model_data.primary_model_primary_stem == INST_STEM:
+                    self.primary_stem = VOCAL_STEM
+                    self.secondary_stem = INST_STEM
+                else:
+                    self.primary_stem = model_data.primary_model_primary_stem
+                    self.secondary_stem = secondary_stem(self.primary_stem)
+
+            self.shifts = model_data.shifts
+            self.is_split_mode = model_data.is_split_mode if not self.demucs_version == DEMUCS_V4 else True
+            self.primary_model_name, self.primary_sources = self.cached_source_callback(DEMUCS_ARCH_TYPE, model_name=self.model_basename)
+
+        if model_data.process_method == VR_ARCH_TYPE:
+            self.check_label_secondary_stem_runs()
+            self.primary_model_name, self.primary_sources = self.cached_source_callback(VR_ARCH_TYPE, model_name=self.model_basename)
+            self.mp = model_data.vr_model_param
+            self.high_end_process = model_data.is_high_end_process
+            self.is_tta = model_data.is_tta
+            self.is_post_process = model_data.is_post_process
+            self.is_gpu_conversion = model_data.is_gpu_conversion
+            self.batch_size = model_data.batch_size
+            self.window_size = model_data.window_size
+            self.input_high_end_h = None
+            self.input_high_end = None
+            self.post_process_threshold = model_data.post_process_threshold
+            self.aggressiveness = {'value': model_data.aggression_setting, 
+                                   'split_bin': self.mp.param['band'][1]['crop_stop'], 
+                                   'aggr_correction': self.mp.param.get('aggr_correction')}
+            
+    def check_label_secondary_stem_runs(self):
+
+        # For ensemble master that's not a 4-stem ensemble, and not mdx_c
+        if self.process_data['is_ensemble_master'] and not self.is_4_stem_ensemble and not self.is_mdx_c:
+            if self.ensemble_primary_stem != self.primary_stem:
+                self.is_primary_stem_only, self.is_secondary_stem_only = self.is_secondary_stem_only, self.is_primary_stem_only
+            
+        # For secondary models
+        if self.is_pre_proc_model or self.is_secondary_model:
+            self.is_primary_stem_only = False
+            self.is_secondary_stem_only = False
+            
+    def start_inference_console_write(self):
+        if self.is_secondary_model and not self.is_pre_proc_model and not self.is_vocal_split_model:
+            self.write_to_console(INFERENCE_STEP_2_SEC(self.process_method, self.model_basename))
+        
+        if self.is_pre_proc_model:
+            self.write_to_console(INFERENCE_STEP_2_PRE(self.process_method, self.model_basename))
+            
+        if self.is_vocal_split_model:
+            self.write_to_console(INFERENCE_STEP_2_VOC_S(self.process_method, self.model_basename))
+        
+    def running_inference_console_write(self, is_no_write=False):
+        self.write_to_console(DONE, base_text='') if not is_no_write else None
+        self.set_progress_bar(0.05) if not is_no_write else None
+        
+        if self.is_secondary_model and not self.is_pre_proc_model and not self.is_vocal_split_model:
+            self.write_to_console(INFERENCE_STEP_1_SEC)
+        elif self.is_pre_proc_model:
+            self.write_to_console(INFERENCE_STEP_1_PRE)
+        elif self.is_vocal_split_model:
+            self.write_to_console(INFERENCE_STEP_1_VOC_S)
+        else:
+            self.write_to_console(INFERENCE_STEP_1)
+        
+    def running_inference_progress_bar(self, length, is_match_mix=False):
+        if not is_match_mix:
+            self.progress_value += 1
+
+            if (0.8/length*self.progress_value) >= 0.8:
+                length = self.progress_value + 1
+  
+            self.set_progress_bar(0.1, (0.8/length*self.progress_value))
+        
+    def load_cached_sources(self):
+        
+        if self.is_secondary_model and not self.is_pre_proc_model:
+            self.write_to_console(INFERENCE_STEP_2_SEC_CACHED_MODOEL(self.process_method, self.model_basename))
+        elif self.is_pre_proc_model:
+            self.write_to_console(INFERENCE_STEP_2_PRE_CACHED_MODOEL(self.process_method, self.model_basename))
+        else:
+            self.write_to_console(INFERENCE_STEP_2_PRIMARY_CACHED, "")
+            
+    def cache_source(self, secondary_sources):
+        
+        model_occurrences = self.list_all_models.count(self.model_basename)
+        
+        if not model_occurrences <= 1:
+            if self.process_method == MDX_ARCH_TYPE:
+                self.cached_model_source_holder(MDX_ARCH_TYPE, secondary_sources, self.model_basename)
+                
+            if self.process_method == VR_ARCH_TYPE:
+                self.cached_model_source_holder(VR_ARCH_TYPE, secondary_sources, self.model_basename)
+
+            if self.process_method == DEMUCS_ARCH_TYPE:
+                self.cached_model_source_holder(DEMUCS_ARCH_TYPE, secondary_sources, self.model_basename)
+           
+    def process_vocal_split_chain(self, sources: dict):
+        
+        def is_valid_vocal_split_condition(master_vocal_source):
+            """Checks if conditions for vocal split processing are met."""
+            conditions = [
+                isinstance(master_vocal_source, np.ndarray),
+                self.vocal_split_model,
+                not self.is_ensemble_mode,
+                not self.is_karaoke,
+                not self.is_bv_model
+            ]
+            return all(conditions)
+        
+        # Retrieve sources from the dictionary with default fallbacks
+        master_inst_source = sources.get(INST_STEM, None)
+        master_vocal_source = sources.get(VOCAL_STEM, None)
+
+        # Process the vocal split chain if conditions are met
+        if is_valid_vocal_split_condition(master_vocal_source):
+            process_chain_model(
+                self.vocal_split_model,
+                self.process_data,
+                vocal_stem_path=self.master_vocal_path,
+                master_vocal_source=master_vocal_source,
+                master_inst_source=master_inst_source
+            )
+  
+    def process_secondary_stem(self, stem_source, secondary_model_source=None, model_scale=None):
+        if not self.is_secondary_model:
+            if self.is_secondary_model_activated and isinstance(secondary_model_source, np.ndarray):
+                secondary_model_scale = model_scale if model_scale else self.secondary_model_scale
+                stem_source = spec_utils.average_dual_sources(stem_source, secondary_model_source, secondary_model_scale)
+  
+        return stem_source
+    
+    def final_process(self, stem_path, source, secondary_source, stem_name, samplerate):
+        source = self.process_secondary_stem(source, secondary_source)
+        self.write_audio(stem_path, source, samplerate, stem_name=stem_name)
+        
+        return {stem_name: source}
+    
+    def write_audio(self, stem_path: str, stem_source, samplerate, stem_name=None):
+        
+        def save_audio_file(path, source):
+            source = spec_utils.normalize(source, self.is_normalization)
+            sf.write(path, source, samplerate, subtype=self.wav_type_set)
+
+            if is_not_ensemble:
+                save_format(path, self.save_format, self.mp3_bit_set)
+
+        def save_voc_split_instrumental(stem_name, stem_source, is_inst_invert=False):
+            inst_stem_name = "Instrumental (With Lead Vocals)" if stem_name == LEAD_VOCAL_STEM else "Instrumental (With Backing Vocals)"
+            inst_stem_path_name = LEAD_VOCAL_STEM_I if stem_name == LEAD_VOCAL_STEM else BV_VOCAL_STEM_I
+            inst_stem_path = self.audio_file_base_voc_split(INST_STEM, inst_stem_path_name)
+            stem_source = -stem_source if is_inst_invert else stem_source
+            inst_stem_source = spec_utils.combine_arrarys([self.master_inst_source, stem_source], is_swap=True)
+            save_with_message(inst_stem_path, inst_stem_name, inst_stem_source)
+
+        def save_voc_split_vocal(stem_name, stem_source):
+            voc_split_stem_name = LEAD_VOCAL_STEM_LABEL if stem_name == LEAD_VOCAL_STEM else BV_VOCAL_STEM_LABEL
+            voc_split_stem_path = self.audio_file_base_voc_split(VOCAL_STEM, stem_name)
+            save_with_message(voc_split_stem_path, voc_split_stem_name, stem_source)
+
+        def save_with_message(stem_path, stem_name, stem_source):
+            is_deverb = self.is_deverb_vocals and (
+                self.deverb_vocal_opt == stem_name or
+                (self.deverb_vocal_opt == 'ALL' and 
+                (stem_name == VOCAL_STEM or stem_name == LEAD_VOCAL_STEM_LABEL or stem_name == BV_VOCAL_STEM_LABEL)))
+
+            self.write_to_console(f'{SAVING_STEM[0]}{stem_name}{SAVING_STEM[1]}')
+            
+            if is_deverb and is_not_ensemble:
+                deverb_vocals(stem_path, stem_source)
+            
+            save_audio_file(stem_path, stem_source)
+            self.write_to_console(DONE, base_text='')
+            
+        def deverb_vocals(stem_path:str, stem_source):
+            self.write_to_console(INFERENCE_STEP_DEVERBING, base_text='')
+            stem_source_deverbed, stem_source_2 = vr_denoiser(stem_source, self.device, is_deverber=True, model_path=self.DEVERBER_MODEL)
+            save_audio_file(stem_path.replace(".wav", "_deverbed.wav"), stem_source_deverbed)
+            save_audio_file(stem_path.replace(".wav", "_reverb_only.wav"), stem_source_2)
+            
+        is_bv_model_lead = (self.is_bv_model_rebalenced and self.is_vocal_split_model and stem_name == LEAD_VOCAL_STEM)
+        is_bv_rebalance_lead = (self.is_bv_model_rebalenced and self.is_vocal_split_model and stem_name == BV_VOCAL_STEM)
+        is_no_vocal_save = self.is_inst_only_voc_splitter and (stem_name == VOCAL_STEM or stem_name == BV_VOCAL_STEM or stem_name == LEAD_VOCAL_STEM) or is_bv_model_lead
+        is_not_ensemble = (not self.is_ensemble_mode or self.is_vocal_split_model)
+        is_do_not_save_inst = (self.is_save_vocal_only and self.is_sec_bv_rebalance and stem_name == INST_STEM)
+
+        if is_bv_rebalance_lead:
+            master_voc_source = spec_utils.match_array_shapes(self.master_vocal_source, stem_source, is_swap=True)
+            bv_rebalance_lead_source = stem_source-master_voc_source
+            
+        if not is_bv_model_lead and not is_do_not_save_inst:
+            if self.is_vocal_split_model or not self.is_secondary_model:
+                if self.is_vocal_split_model and not self.is_inst_only_voc_splitter:
+                    save_voc_split_vocal(stem_name, stem_source)
+                    if is_bv_rebalance_lead:
+                        save_voc_split_vocal(LEAD_VOCAL_STEM, bv_rebalance_lead_source)
+                else:
+                    if not is_no_vocal_save:
+                        save_with_message(stem_path, stem_name, stem_source)
+                    
+                if self.is_save_inst_vocal_splitter and not self.is_save_vocal_only:
+                    save_voc_split_instrumental(stem_name, stem_source)
+                    if is_bv_rebalance_lead:
+                        save_voc_split_instrumental(LEAD_VOCAL_STEM, bv_rebalance_lead_source, is_inst_invert=True)
+
+                self.set_progress_bar(0.95)
+
+        if stem_name == VOCAL_STEM:
+            self.master_vocal_path = stem_path
+
+    def pitch_fix(self, source, sr_pitched, org_mix):
+        semitone_shift = self.semitone_shift
+        source = spec_utils.change_pitch_semitones(source, sr_pitched, semitone_shift=semitone_shift)[0]
+        source = spec_utils.match_array_shapes(source, org_mix)
+        return source
+    
+    def match_frequency_pitch(self, mix):
+        source = mix
+        if self.is_match_frequency_pitch and self.is_pitch_change:
+            source, sr_pitched = spec_utils.change_pitch_semitones(mix, 44100, semitone_shift=-self.semitone_shift)
+            source = self.pitch_fix(source, sr_pitched, mix)
+
+        return source
+
+class SeperateMDX(SeperateAttributes):        
+
+    def seperate(self):
+        samplerate = 44100
+    
+        if self.primary_model_name == self.model_basename and isinstance(self.primary_sources, tuple):
+            mix, source = self.primary_sources
+            self.load_cached_sources()
+        else:
+            self.start_inference_console_write()
+
+            if self.is_mdx_ckpt:
+                model_params = torch.load(self.model_path, map_location=lambda storage, loc: storage)['hyper_parameters']
+                self.dim_c, self.hop = model_params['dim_c'], model_params['hop_length']
+                separator = MdxnetSet.ConvTDFNet(**model_params)
+                self.model_run = separator.load_from_checkpoint(self.model_path).to(self.device).eval()
+            else:
+                if self.mdx_segment_size == self.dim_t and not self.is_other_gpu:
+                    ort_ = ort.InferenceSession(self.model_path, providers=self.run_type)
+                    self.model_run = lambda spek:ort_.run(None, {'input': spek.cpu().numpy()})[0]
+                else:
+                    self.model_run = ConvertModel(load(self.model_path))
+                    self.model_run.to(self.device).eval()
+
+            self.running_inference_console_write()
+            mix = prepare_mix(self.audio_file)
+            
+            source = self.demix(mix)
+            
+            if not self.is_vocal_split_model:
+                self.cache_source((mix, source))
+            self.write_to_console(DONE, base_text='')            
+
+        mdx_net_cut = True if self.primary_stem in MDX_NET_FREQ_CUT and self.is_match_frequency_pitch else False
+
+        if self.is_secondary_model_activated and self.secondary_model:
+            self.secondary_source_primary, self.secondary_source_secondary = process_secondary_model(self.secondary_model, self.process_data, main_process_method=self.process_method, main_model_primary=self.primary_stem)
+        
+        if not self.is_primary_stem_only:
+            secondary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({self.secondary_stem}).wav')
+            if not isinstance(self.secondary_source, np.ndarray):
+                raw_mix = self.demix(self.match_frequency_pitch(mix), is_match_mix=True) if mdx_net_cut else self.match_frequency_pitch(mix)
+                self.secondary_source = spec_utils.invert_stem(raw_mix, source) if self.is_invert_spec else mix.T-source.T
+            
+            self.secondary_source_map = self.final_process(secondary_stem_path, self.secondary_source, self.secondary_source_secondary, self.secondary_stem, samplerate)
+        
+        if not self.is_secondary_stem_only:
+            primary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({self.primary_stem}).wav')
+
+            if not isinstance(self.primary_source, np.ndarray):
+                self.primary_source = source.T
+                
+            self.primary_source_map = self.final_process(primary_stem_path, self.primary_source, self.secondary_source_primary, self.primary_stem, samplerate)
+        
+        clear_gpu_cache()
+
+        secondary_sources = {**self.primary_source_map, **self.secondary_source_map}
+        
+        self.process_vocal_split_chain(secondary_sources)
+
+        if self.is_secondary_model or self.is_pre_proc_model:
+            return secondary_sources
+
+    def initialize_model_settings(self):
+        self.n_bins = self.n_fft//2+1
+        self.trim = self.n_fft//2
+        self.chunk_size = self.hop * (self.mdx_segment_size-1)
+        self.gen_size = self.chunk_size-2*self.trim
+        self.stft = STFT(self.n_fft, self.hop, self.dim_f, self.device)
+
+    def demix(self, mix, is_match_mix=False):
+        self.initialize_model_settings()
+        
+        org_mix = mix
+        tar_waves_ = []
+
+        if is_match_mix:
+            chunk_size = self.hop * (256-1)
+            overlap = 0.02
+        else:
+            chunk_size = self.chunk_size
+            overlap = self.overlap_mdx
+            
+            if self.is_pitch_change:
+                mix, sr_pitched = spec_utils.change_pitch_semitones(mix, 44100, semitone_shift=-self.semitone_shift)
+
+        gen_size = chunk_size-2*self.trim
+
+        pad = gen_size + self.trim - ((mix.shape[-1]) % gen_size)
+        mixture = np.concatenate((np.zeros((2, self.trim), dtype='float32'), mix, np.zeros((2, pad), dtype='float32')), 1)
+
+        step = self.chunk_size - self.n_fft if overlap == DEFAULT else int((1 - overlap) * chunk_size)
+        result = np.zeros((1, 2, mixture.shape[-1]), dtype=np.float32)
+        divider = np.zeros((1, 2, mixture.shape[-1]), dtype=np.float32)
+        total = 0
+        total_chunks = (mixture.shape[-1] + step - 1) // step
+
+        for i in range(0, mixture.shape[-1], step):
+            total += 1
+            start = i
+            end = min(i + chunk_size, mixture.shape[-1])
+
+            chunk_size_actual = end - start
+
+            if overlap == 0:
+                window = None
+            else:
+                window = np.hanning(chunk_size_actual)
+                window = np.tile(window[None, None, :], (1, 2, 1))
+
+            mix_part_ = mixture[:, start:end]
+            if end != i + chunk_size:
+                pad_size = (i + chunk_size) - end
+                mix_part_ = np.concatenate((mix_part_, np.zeros((2, pad_size), dtype='float32')), axis=-1)
+
+            mix_part = torch.tensor([mix_part_], dtype=torch.float32).to(self.device)
+            mix_waves = mix_part.split(self.mdx_batch_size)
+            
+            with torch.no_grad():
+                for mix_wave in mix_waves:
+                    self.running_inference_progress_bar(total_chunks, is_match_mix=is_match_mix)
+
+                    tar_waves = self.run_model(mix_wave, is_match_mix=is_match_mix)
+                    
+                    if window is not None:
+                        tar_waves[..., :chunk_size_actual] *= window 
+                        divider[..., start:end] += window
+                    else:
+                        divider[..., start:end] += 1
+
+                    result[..., start:end] += tar_waves[..., :end-start]
+            
+        tar_waves = result / divider
+        tar_waves_.append(tar_waves)
+
+        tar_waves_ = np.vstack(tar_waves_)[:, :, self.trim:-self.trim]
+        tar_waves = np.concatenate(tar_waves_, axis=-1)[:, :mix.shape[-1]]
+        
+        source = tar_waves[:,0:None]
+
+        if self.is_pitch_change and not is_match_mix:
+            source = self.pitch_fix(source, sr_pitched, org_mix)
+
+        source = source if is_match_mix else source*self.compensate
+
+        if self.is_denoise_model and not is_match_mix:
+            if NO_STEM in self.primary_stem_native or self.primary_stem_native == INST_STEM:
+                if org_mix.shape[1] != source.shape[1]:
+                    source = spec_utils.match_array_shapes(source, org_mix)
+                source = org_mix - vr_denoiser(org_mix-source, self.device, model_path=self.DENOISER_MODEL)
+            else:
+                source = vr_denoiser(source, self.device, model_path=self.DENOISER_MODEL)
+
+        return source
+
+    def run_model(self, mix, is_match_mix=False):
+        
+        spek = self.stft(mix.to(self.device))*self.adjust
+        spek[:, :, :3, :] *= 0 
+
+        if is_match_mix:
+            spec_pred = spek.cpu().numpy()
+        else:
+            spec_pred = -self.model_run(-spek)*0.5+self.model_run(spek)*0.5 if self.is_denoise else self.model_run(spek)
+
+        return self.stft.inverse(torch.tensor(spec_pred).to(self.device)).cpu().detach().numpy()
+
+class SeperateMDXC(SeperateAttributes):        
+
+    def seperate(self):
+        samplerate = 44100
+        sources = None
+
+        if self.primary_model_name == self.model_basename and isinstance(self.primary_sources, tuple):
+            mix, sources = self.primary_sources
+            self.load_cached_sources()
+        else:
+            self.start_inference_console_write()
+            self.running_inference_console_write()
+            mix = prepare_mix(self.audio_file)
+            sources = self.demix(mix)
+            if not self.is_vocal_split_model:
+                self.cache_source((mix, sources))
+            self.write_to_console(DONE, base_text='')
+
+        stem_list = [self.mdx_c_configs.training.target_instrument] if self.mdx_c_configs.training.target_instrument else [i for i in self.mdx_c_configs.training.instruments]
+
+        if self.is_secondary_model:
+            if self.is_pre_proc_model:
+                self.mdxnet_stem_select = stem_list[0]
+            else:
+                self.mdxnet_stem_select = self.main_model_primary_stem_4_stem if self.main_model_primary_stem_4_stem else self.primary_model_primary_stem
+            self.primary_stem = self.mdxnet_stem_select
+            self.secondary_stem = secondary_stem(self.mdxnet_stem_select)
+            self.is_primary_stem_only, self.is_secondary_stem_only = False, False
+
+        is_all_stems = self.mdxnet_stem_select == ALL_STEMS
+        is_not_ensemble_master = not self.process_data['is_ensemble_master']
+        is_not_single_stem = not len(stem_list) <= 2
+        is_not_secondary_model = not self.is_secondary_model
+        is_ensemble_4_stem = self.is_4_stem_ensemble and is_not_single_stem
+
+        if (is_all_stems and is_not_ensemble_master and is_not_single_stem and is_not_secondary_model) or is_ensemble_4_stem and not self.is_pre_proc_model:
+            for stem in stem_list:
+                primary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({stem}).wav')
+                self.primary_source = sources[stem].T
+                self.write_audio(primary_stem_path, self.primary_source, samplerate, stem_name=stem)
+                
+                if stem == VOCAL_STEM and not self.is_sec_bv_rebalance:
+                    self.process_vocal_split_chain({VOCAL_STEM:stem})
+        else:
+            if len(stem_list) == 1:
+                source_primary = sources  
+            else:
+                source_primary = sources[stem_list[0]] if self.is_multi_stem_ensemble and len(stem_list) == 2 else sources[self.mdxnet_stem_select]
+            if self.is_secondary_model_activated and self.secondary_model:
+                self.secondary_source_primary, self.secondary_source_secondary = process_secondary_model(self.secondary_model, 
+                                                                                                         self.process_data, 
+                                                                                                         main_process_method=self.process_method, 
+                                                                                                         main_model_primary=self.primary_stem)
+
+            if not self.is_primary_stem_only:
+                secondary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({self.secondary_stem}).wav')
+                if not isinstance(self.secondary_source, np.ndarray):
+                    
+                    if self.is_mdx_combine_stems and len(stem_list) >= 2:
+                        if len(stem_list) == 2:
+                            secondary_source = sources[self.secondary_stem]
+                        else:
+                            sources.pop(self.primary_stem)
+                            next_stem = next(iter(sources))
+                            secondary_source = np.zeros_like(sources[next_stem])
+                            for v in sources.values():
+                                secondary_source += v
+                                
+                        self.secondary_source = secondary_source.T 
+                    else:
+                        self.secondary_source, raw_mix = source_primary, self.match_frequency_pitch(mix)
+                        self.secondary_source = spec_utils.to_shape(self.secondary_source, raw_mix.shape)
+                    
+                        if self.is_invert_spec:
+                            self.secondary_source = spec_utils.invert_stem(raw_mix, self.secondary_source)
+                        else:
+                            self.secondary_source = (-self.secondary_source.T+raw_mix.T)
+                            
+                self.secondary_source_map = self.final_process(secondary_stem_path, self.secondary_source, self.secondary_source_secondary, self.secondary_stem, samplerate)    
+
+            if not self.is_secondary_stem_only:
+                primary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({self.primary_stem}).wav')
+                if not isinstance(self.primary_source, np.ndarray):
+                    self.primary_source = source_primary.T
+
+                self.primary_source_map = self.final_process(primary_stem_path, self.primary_source, self.secondary_source_primary, self.primary_stem, samplerate)
+
+        clear_gpu_cache()
+        
+        secondary_sources = {**self.primary_source_map, **self.secondary_source_map}
+        self.process_vocal_split_chain(secondary_sources)
+        
+        if self.is_secondary_model or self.is_pre_proc_model:
+            return secondary_sources
+
+    def demix(self, mix):
+        sr_pitched = 441000
+        org_mix = mix
+        if self.is_pitch_change:
+            mix, sr_pitched = spec_utils.change_pitch_semitones(mix, 44100, semitone_shift=-self.semitone_shift)
+
+        model = TFC_TDF_net(self.mdx_c_configs, device=self.device)
+        model.load_state_dict(torch.load(self.model_path, map_location=cpu))
+        model.to(self.device).eval()
+        mix = torch.tensor(mix, dtype=torch.float32)
+
+        try:
+            S = model.num_target_instruments
+        except Exception as e:
+            S = model.module.num_target_instruments
+
+        mdx_segment_size = self.mdx_c_configs.inference.dim_t if self.is_mdx_c_seg_def else self.mdx_segment_size
+        
+        batch_size = self.mdx_batch_size
+        chunk_size = self.mdx_c_configs.audio.hop_length * (mdx_segment_size - 1)
+        overlap = self.overlap_mdx23
+
+        hop_size = chunk_size // overlap
+        mix_shape = mix.shape[1]
+        pad_size = hop_size - (mix_shape - chunk_size) % hop_size
+        mix = torch.cat([torch.zeros(2, chunk_size - hop_size), mix, torch.zeros(2, pad_size + chunk_size - hop_size)], 1)
+
+        chunks = mix.unfold(1, chunk_size, hop_size).transpose(0, 1)
+        batches = [chunks[i : i + batch_size] for i in range(0, len(chunks), batch_size)]
+        
+        X = torch.zeros(S, *mix.shape) if S > 1 else torch.zeros_like(mix)
+        X = X.to(self.device)
+
+        with torch.no_grad():
+            cnt = 0
+            for batch in batches:
+                self.running_inference_progress_bar(len(batches))
+                x = model(batch.to(self.device))
+                
+                for w in x:
+                    X[..., cnt * hop_size : cnt * hop_size + chunk_size] += w
+                    cnt += 1
+
+        estimated_sources = X[..., chunk_size - hop_size:-(pad_size + chunk_size - hop_size)] / overlap
+        del X
+        pitch_fix = lambda s:self.pitch_fix(s, sr_pitched, org_mix)
+
+        if S > 1:
+            sources = {k: pitch_fix(v) if self.is_pitch_change else v for k, v in zip(self.mdx_c_configs.training.instruments, estimated_sources.cpu().detach().numpy())}
+            del estimated_sources
+            if self.is_denoise_model:
+                if VOCAL_STEM in sources.keys() and INST_STEM in sources.keys():
+                    sources[VOCAL_STEM] = vr_denoiser(sources[VOCAL_STEM], self.device, model_path=self.DENOISER_MODEL)
+                    if sources[VOCAL_STEM].shape[1] != org_mix.shape[1]:
+                        sources[VOCAL_STEM] = spec_utils.match_array_shapes(sources[VOCAL_STEM], org_mix)
+                    sources[INST_STEM] = org_mix - sources[VOCAL_STEM]
+                            
+            return sources
+        else:
+            est_s = estimated_sources.cpu().detach().numpy()
+            del estimated_sources
+            return pitch_fix(est_s) if self.is_pitch_change else est_s
+
+class SeperateDemucs(SeperateAttributes):
+    def seperate(self):
+        samplerate = 44100
+        source = None
+        model_scale = None
+        stem_source = None
+        stem_source_secondary = None
+        inst_mix = None
+        inst_source = None
+        is_no_write = False
+        is_no_piano_guitar = False
+        is_no_cache = False
+        
+        if self.primary_model_name == self.model_basename and isinstance(self.primary_sources, np.ndarray) and not self.pre_proc_model:
+            source = self.primary_sources
+            self.load_cached_sources()
+        else:
+            self.start_inference_console_write()
+            is_no_cache = True
+
+        mix = prepare_mix(self.audio_file)
+
+        if is_no_cache:
+            if self.demucs_version == DEMUCS_V1:
+                if str(self.model_path).endswith(".gz"):
+                    self.model_path = gzip.open(self.model_path, "rb")
+                klass, args, kwargs, state = torch.load(self.model_path)
+                self.demucs = klass(*args, **kwargs)
+                self.demucs.to(self.device) 
+                self.demucs.load_state_dict(state)
+            elif self.demucs_version == DEMUCS_V2:
+                self.demucs = auto_load_demucs_model_v2(self.demucs_source_list, self.model_path)
+                self.demucs.to(self.device) 
+                self.demucs.load_state_dict(torch.load(self.model_path))
+                self.demucs.eval()
+            else:  
+                self.demucs = HDemucs(sources=self.demucs_source_list)
+                self.demucs = _gm(name=os.path.splitext(os.path.basename(self.model_path))[0], 
+                                  repo=Path(os.path.dirname(self.model_path)))
+                self.demucs = demucs_segments(self.segment, self.demucs)
+                self.demucs.to(self.device)
+                self.demucs.eval()
+
+            if self.pre_proc_model:
+                if self.primary_stem not in [VOCAL_STEM, INST_STEM]:
+                    is_no_write = True
+                    self.write_to_console(DONE, base_text='')
+                    mix_no_voc = process_secondary_model(self.pre_proc_model, self.process_data, is_pre_proc_model=True)
+                    inst_mix = prepare_mix(mix_no_voc[INST_STEM])
+                    self.process_iteration()
+                    self.running_inference_console_write(is_no_write=is_no_write)
+                    inst_source = self.demix_demucs(inst_mix)
+                    self.process_iteration()
+
+            self.running_inference_console_write(is_no_write=is_no_write) if not self.pre_proc_model else None
+            
+            if self.primary_model_name == self.model_basename and isinstance(self.primary_sources, np.ndarray) and self.pre_proc_model:
+                source = self.primary_sources
+            else:
+                source = self.demix_demucs(mix)
+            
+            self.write_to_console(DONE, base_text='')
+            
+            del self.demucs
+            clear_gpu_cache()
+            
+        if isinstance(inst_source, np.ndarray):
+            source_reshape = spec_utils.reshape_sources(inst_source[self.demucs_source_map[VOCAL_STEM]], source[self.demucs_source_map[VOCAL_STEM]])
+            inst_source[self.demucs_source_map[VOCAL_STEM]] = source_reshape
+            source = inst_source
+
+        if isinstance(source, np.ndarray):
+            
+            if len(source) == 2:
+                self.demucs_source_map = DEMUCS_2_SOURCE_MAPPER
+            else:
+                self.demucs_source_map = DEMUCS_6_SOURCE_MAPPER if len(source) == 6 else DEMUCS_4_SOURCE_MAPPER
+
+                if len(source) == 6 and self.process_data['is_ensemble_master'] or len(source) == 6 and self.is_secondary_model:
+                    is_no_piano_guitar = True
+                    six_stem_other_source = list(source)
+                    six_stem_other_source = [i for n, i in enumerate(source) if n in [self.demucs_source_map[OTHER_STEM], self.demucs_source_map[GUITAR_STEM], self.demucs_source_map[PIANO_STEM]]]
+                    other_source = np.zeros_like(six_stem_other_source[0])
+                    for i in six_stem_other_source:
+                        other_source += i
+                    source_reshape = spec_utils.reshape_sources(source[self.demucs_source_map[OTHER_STEM]], other_source)
+                    source[self.demucs_source_map[OTHER_STEM]] = source_reshape
+                    
+        if not self.is_vocal_split_model:
+            self.cache_source(source)
+        
+        if (self.demucs_stems == ALL_STEMS and not self.process_data['is_ensemble_master']) or self.is_4_stem_ensemble and not self.is_return_dual:
+            for stem_name, stem_value in self.demucs_source_map.items():
+                if self.is_secondary_model_activated and not self.is_secondary_model and not stem_value >= 4:
+                    if self.secondary_model_4_stem[stem_value]:
+                        model_scale = self.secondary_model_4_stem_scale[stem_value]
+                        stem_source_secondary = process_secondary_model(self.secondary_model_4_stem[stem_value], self.process_data, main_model_primary_stem_4_stem=stem_name, is_source_load=True, is_return_dual=False)
+                        if isinstance(stem_source_secondary, np.ndarray):
+                            stem_source_secondary = stem_source_secondary[1 if self.secondary_model_4_stem[stem_value].demucs_stem_count == 2 else stem_value].T
+                        elif type(stem_source_secondary) is dict:
+                            stem_source_secondary = stem_source_secondary[stem_name]
+                            
+                stem_source_secondary = None if stem_value >= 4 else stem_source_secondary
+                stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({stem_name}).wav')
+                stem_source = source[stem_value].T
+                
+                stem_source = self.process_secondary_stem(stem_source, secondary_model_source=stem_source_secondary, model_scale=model_scale)
+                self.write_audio(stem_path, stem_source, samplerate, stem_name=stem_name)
+                
+                if stem_name == VOCAL_STEM and not self.is_sec_bv_rebalance:
+                    self.process_vocal_split_chain({VOCAL_STEM:stem_source})
+                
+            if self.is_secondary_model:    
+                return source
+        else:
+            if self.is_secondary_model_activated and self.secondary_model:
+                    self.secondary_source_primary, self.secondary_source_secondary = process_secondary_model(self.secondary_model, self.process_data, main_process_method=self.process_method)
+                    
+            if not self.is_primary_stem_only:
+                def secondary_save(sec_stem_name, source, raw_mixture=None, is_inst_mixture=False):
+                    secondary_source = self.secondary_source if not is_inst_mixture else None
+                    secondary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({sec_stem_name}).wav')
+                    secondary_source_secondary = None
+                    
+                    if not isinstance(secondary_source, np.ndarray):
+                        if self.is_demucs_combine_stems:
+                            source = list(source)
+                            if is_inst_mixture:
+                                source = [i for n, i in enumerate(source) if not n in [self.demucs_source_map[self.primary_stem], self.demucs_source_map[VOCAL_STEM]]]
+                            else:
+                                source.pop(self.demucs_source_map[self.primary_stem])
+                                
+                            source = source[:len(source) - 2] if is_no_piano_guitar else source
+                            secondary_source = np.zeros_like(source[0])
+                            for i in source:
+                                secondary_source += i
+                            secondary_source = secondary_source.T
+                        else:
+                            if not isinstance(raw_mixture, np.ndarray):
+                                raw_mixture = prepare_mix(self.audio_file)
+       
+                            secondary_source = source[self.demucs_source_map[self.primary_stem]]
+                            
+                            if self.is_invert_spec:
+                                secondary_source = spec_utils.invert_stem(raw_mixture, secondary_source)
+                            else:
+                                raw_mixture = spec_utils.reshape_sources(secondary_source, raw_mixture)
+                                secondary_source = (-secondary_source.T+raw_mixture.T)
+                            
+                    if not is_inst_mixture:
+                        self.secondary_source = secondary_source
+                        secondary_source_secondary = self.secondary_source_secondary
+                        self.secondary_source = self.process_secondary_stem(secondary_source, secondary_source_secondary)
+                        self.secondary_source_map = {self.secondary_stem: self.secondary_source}
+
+                    self.write_audio(secondary_stem_path, secondary_source, samplerate, stem_name=sec_stem_name)
+
+                secondary_save(self.secondary_stem, source, raw_mixture=mix)
+                
+                if self.is_demucs_pre_proc_model_inst_mix and self.pre_proc_model and not self.is_4_stem_ensemble:
+                    secondary_save(f"{self.secondary_stem} {INST_STEM}", source, raw_mixture=inst_mix, is_inst_mixture=True)
+
+            if not self.is_secondary_stem_only:
+                primary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({self.primary_stem}).wav')
+                if not isinstance(self.primary_source, np.ndarray):
+                    self.primary_source = source[self.demucs_source_map[self.primary_stem]].T
+                
+                self.primary_source_map = self.final_process(primary_stem_path, self.primary_source, self.secondary_source_primary, self.primary_stem, samplerate)
+
+            secondary_sources = {**self.primary_source_map, **self.secondary_source_map}
+            
+            self.process_vocal_split_chain(secondary_sources)
+            
+            if self.is_secondary_model:    
+                return secondary_sources
+    
+    def demix_demucs(self, mix):
+        
+        org_mix = mix
+        
+        if self.is_pitch_change:
+            mix, sr_pitched = spec_utils.change_pitch_semitones(mix, 44100, semitone_shift=-self.semitone_shift)
+        
+        processed = {}
+        mix = torch.tensor(mix, dtype=torch.float32)
+        ref = mix.mean(0)        
+        mix = (mix - ref.mean()) / ref.std()
+        mix_infer = mix 
+        
+        with torch.no_grad():
+            if self.demucs_version == DEMUCS_V1:
+                sources = apply_model_v1(self.demucs, 
+                                            mix_infer.to(self.device), 
+                                            self.shifts, 
+                                            self.is_split_mode,
+                                            set_progress_bar=self.set_progress_bar)
+            elif self.demucs_version == DEMUCS_V2:
+                sources = apply_model_v2(self.demucs, 
+                                            mix_infer.to(self.device), 
+                                            self.shifts,
+                                            self.is_split_mode,
+                                            self.overlap,
+                                            set_progress_bar=self.set_progress_bar)
+            else:
+                sources = apply_model(self.demucs, 
+                                        mix_infer[None], 
+                                        self.shifts,
+                                        self.is_split_mode,
+                                        self.overlap,
+                                        static_shifts=1 if self.shifts == 0 else self.shifts,
+                                        set_progress_bar=self.set_progress_bar,
+                                        device=self.device)[0]
+        
+        sources = (sources * ref.std() + ref.mean()).cpu().numpy()
+        sources[[0,1]] = sources[[1,0]]
+        processed[mix] = sources[:,:,0:None].copy()
+        sources = list(processed.values())
+        sources = [s[:,:,0:None] for s in sources]
+        #sources = [self.pitch_fix(s[:,:,0:None], sr_pitched, org_mix) if self.is_pitch_change else s[:,:,0:None] for s in sources]
+        sources = np.concatenate(sources, axis=-1)
+                     
+        if self.is_pitch_change:
+            sources = np.stack([self.pitch_fix(stem, sr_pitched, org_mix) for stem in sources])
+                        
+        return sources
+
+class SeperateVR(SeperateAttributes):        
+
+    def seperate(self):
+        if self.primary_model_name == self.model_basename and isinstance(self.primary_sources, tuple):
+            y_spec, v_spec = self.primary_sources
+            self.load_cached_sources()
+        else:
+            self.start_inference_console_write()
+
+            device = self.device
+
+            nn_arch_sizes = [
+                31191, # default
+                33966, 56817, 123821, 123812, 129605, 218409, 537238, 537227]
+            vr_5_1_models = [56817, 218409]
+            model_size = math.ceil(os.stat(self.model_path).st_size / 1024)
+            nn_arch_size = min(nn_arch_sizes, key=lambda x:abs(x-model_size))
+
+            if nn_arch_size in vr_5_1_models or self.is_vr_51_model:
+                self.model_run = nets_new.CascadedNet(self.mp.param['bins'] * 2, 
+                                                      nn_arch_size, 
+                                                      nout=self.model_capacity[0], 
+                                                      nout_lstm=self.model_capacity[1])
+                self.is_vr_51_model = True
+            else:
+                self.model_run = nets.determine_model_capacity(self.mp.param['bins'] * 2, nn_arch_size)
+                            
+            self.model_run.load_state_dict(torch.load(self.model_path, map_location=cpu)) 
+            self.model_run.to(device) 
+
+            self.running_inference_console_write()
+                        
+            y_spec, v_spec = self.inference_vr(self.loading_mix(), device, self.aggressiveness)
+            if not self.is_vocal_split_model:
+                self.cache_source((y_spec, v_spec))
+            self.write_to_console(DONE, base_text='')
+            
+        if self.is_secondary_model_activated and self.secondary_model:
+            self.secondary_source_primary, self.secondary_source_secondary = process_secondary_model(self.secondary_model, self.process_data, main_process_method=self.process_method, main_model_primary=self.primary_stem)
+
+        if not self.is_secondary_stem_only:
+            primary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({self.primary_stem}).wav')
+            if not isinstance(self.primary_source, np.ndarray):
+                self.primary_source = self.spec_to_wav(y_spec).T
+                if not self.model_samplerate == 44100:
+                    self.primary_source = librosa.resample(self.primary_source.T, orig_sr=self.model_samplerate, target_sr=44100).T
+                
+            self.primary_source_map = self.final_process(primary_stem_path, self.primary_source, self.secondary_source_primary, self.primary_stem, 44100)  
+
+        if not self.is_primary_stem_only:
+            secondary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({self.secondary_stem}).wav')
+            if not isinstance(self.secondary_source, np.ndarray):
+                self.secondary_source = self.spec_to_wav(v_spec).T
+                if not self.model_samplerate == 44100:
+                    self.secondary_source = librosa.resample(self.secondary_source.T, orig_sr=self.model_samplerate, target_sr=44100).T
+            
+            self.secondary_source_map = self.final_process(secondary_stem_path, self.secondary_source, self.secondary_source_secondary, self.secondary_stem, 44100)
+            
+        clear_gpu_cache()
+        secondary_sources = {**self.primary_source_map, **self.secondary_source_map}
+        
+        self.process_vocal_split_chain(secondary_sources)
+        
+        if self.is_secondary_model:
+            return secondary_sources
+            
+    def loading_mix(self):
+
+        X_wave, X_spec_s = {}, {}
+        
+        bands_n = len(self.mp.param['band'])
+        
+        audio_file = spec_utils.write_array_to_mem(self.audio_file, subtype=self.wav_type_set)
+        is_mp3 = audio_file.endswith('.mp3') if isinstance(audio_file, str) else False
+
+        for d in range(bands_n, 0, -1):        
+            bp = self.mp.param['band'][d]
+        
+            if OPERATING_SYSTEM == 'Darwin':
+                wav_resolution = 'polyphase' if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else bp['res_type']
+            else:
+                wav_resolution = bp['res_type']
+        
+            if d == bands_n: # high-end band
+                X_wave[d], _ = librosa.load(audio_file, bp['sr'], False, dtype=np.float32, res_type=wav_resolution)
+                X_spec_s[d] = spec_utils.wave_to_spectrogram(X_wave[d], bp['hl'], bp['n_fft'], self.mp, band=d, is_v51_model=self.is_vr_51_model)
+                    
+                if not np.any(X_wave[d]) and is_mp3:
+                    X_wave[d] = rerun_mp3(audio_file, bp['sr'])
+
+                if X_wave[d].ndim == 1:
+                    X_wave[d] = np.asarray([X_wave[d], X_wave[d]])
+            else: # lower bands
+                X_wave[d] = librosa.resample(X_wave[d+1], self.mp.param['band'][d+1]['sr'], bp['sr'], res_type=wav_resolution)
+                X_spec_s[d] = spec_utils.wave_to_spectrogram(X_wave[d], bp['hl'], bp['n_fft'], self.mp, band=d, is_v51_model=self.is_vr_51_model)
+
+            if d == bands_n and self.high_end_process != 'none':
+                self.input_high_end_h = (bp['n_fft']//2 - bp['crop_stop']) + (self.mp.param['pre_filter_stop'] - self.mp.param['pre_filter_start'])
+                self.input_high_end = X_spec_s[d][:, bp['n_fft']//2-self.input_high_end_h:bp['n_fft']//2, :]
+
+        X_spec = spec_utils.combine_spectrograms(X_spec_s, self.mp, is_v51_model=self.is_vr_51_model)
+        
+        del X_wave, X_spec_s, audio_file
+
+        return X_spec
+
+    def inference_vr(self, X_spec, device, aggressiveness):
+        def _execute(X_mag_pad, roi_size):
+            X_dataset = []
+            patches = (X_mag_pad.shape[2] - 2 * self.model_run.offset) // roi_size
+            total_iterations = patches//self.batch_size if not self.is_tta else (patches//self.batch_size)*2
+            for i in range(patches):
+                start = i * roi_size
+                X_mag_window = X_mag_pad[:, :, start:start + self.window_size]
+                X_dataset.append(X_mag_window)
+
+            X_dataset = np.asarray(X_dataset)
+            self.model_run.eval()
+            with torch.no_grad():
+                mask = []
+                for i in range(0, patches, self.batch_size):
+                    self.progress_value += 1
+                    if self.progress_value >= total_iterations:
+                        self.progress_value = total_iterations
+                    self.set_progress_bar(0.1, 0.8/total_iterations*self.progress_value)
+                    X_batch = X_dataset[i: i + self.batch_size]
+                    X_batch = torch.from_numpy(X_batch).to(device)
+                    pred = self.model_run.predict_mask(X_batch)
+                    if not pred.size()[3] > 0:
+                        raise Exception(ERROR_MAPPER[WINDOW_SIZE_ERROR])
+                    pred = pred.detach().cpu().numpy()
+                    pred = np.concatenate(pred, axis=2)
+                    mask.append(pred)
+                if len(mask) == 0:
+                    raise Exception(ERROR_MAPPER[WINDOW_SIZE_ERROR])
+                
+                mask = np.concatenate(mask, axis=2)
+            return mask
+
+        def postprocess(mask, X_mag, X_phase):
+            is_non_accom_stem = False
+            for stem in NON_ACCOM_STEMS:
+                if stem == self.primary_stem:
+                    is_non_accom_stem = True
+                    
+            mask = spec_utils.adjust_aggr(mask, is_non_accom_stem, aggressiveness)
+
+            if self.is_post_process:
+                mask = spec_utils.merge_artifacts(mask, thres=self.post_process_threshold)
+
+            y_spec = mask * X_mag * np.exp(1.j * X_phase)
+            v_spec = (1 - mask) * X_mag * np.exp(1.j * X_phase)
+        
+            return y_spec, v_spec
+        
+        X_mag, X_phase = spec_utils.preprocess(X_spec)
+        n_frame = X_mag.shape[2]
+        pad_l, pad_r, roi_size = spec_utils.make_padding(n_frame, self.window_size, self.model_run.offset)
+        X_mag_pad = np.pad(X_mag, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
+        X_mag_pad /= X_mag_pad.max()
+        mask = _execute(X_mag_pad, roi_size)
+        
+        if self.is_tta:
+            pad_l += roi_size // 2
+            pad_r += roi_size // 2
+            X_mag_pad = np.pad(X_mag, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
+            X_mag_pad /= X_mag_pad.max()
+            mask_tta = _execute(X_mag_pad, roi_size)
+            mask_tta = mask_tta[:, :, roi_size // 2:]
+            mask = (mask[:, :, :n_frame] + mask_tta[:, :, :n_frame]) * 0.5
+        else:
+            mask = mask[:, :, :n_frame]
+
+        y_spec, v_spec = postprocess(mask, X_mag, X_phase)
+        
+        return y_spec, v_spec
+
+    def spec_to_wav(self, spec):
+        if self.high_end_process.startswith('mirroring') and isinstance(self.input_high_end, np.ndarray) and self.input_high_end_h:        
+            input_high_end_ = spec_utils.mirroring(self.high_end_process, spec, self.input_high_end, self.mp)
+            wav = spec_utils.cmb_spectrogram_to_wave(spec, self.mp, self.input_high_end_h, input_high_end_, is_v51_model=self.is_vr_51_model)       
+        else:
+            wav = spec_utils.cmb_spectrogram_to_wave(spec, self.mp, is_v51_model=self.is_vr_51_model)
+            
+        return wav
+
+def process_secondary_model(secondary_model: ModelData, 
+                            process_data, 
+                            main_model_primary_stem_4_stem=None, 
+                            is_source_load=False, 
+                            main_process_method=None, 
+                            is_pre_proc_model=False, 
+                            is_return_dual=True, 
+                            main_model_primary=None):
+        
+    if not is_pre_proc_model:
+        process_iteration = process_data['process_iteration']
+        process_iteration()
+    
+    if secondary_model.process_method == VR_ARCH_TYPE:
+        seperator = SeperateVR(secondary_model, process_data, main_model_primary_stem_4_stem=main_model_primary_stem_4_stem, main_process_method=main_process_method, main_model_primary=main_model_primary)
+    if secondary_model.process_method == MDX_ARCH_TYPE:
+        if secondary_model.is_mdx_c:
+            seperator = SeperateMDXC(secondary_model, process_data, main_model_primary_stem_4_stem=main_model_primary_stem_4_stem, main_process_method=main_process_method, is_return_dual=is_return_dual, main_model_primary=main_model_primary)
+        else:
+            seperator = SeperateMDX(secondary_model, process_data, main_model_primary_stem_4_stem=main_model_primary_stem_4_stem, main_process_method=main_process_method, main_model_primary=main_model_primary)
+    if secondary_model.process_method == DEMUCS_ARCH_TYPE:
+        seperator = SeperateDemucs(secondary_model, process_data, main_model_primary_stem_4_stem=main_model_primary_stem_4_stem, main_process_method=main_process_method, is_return_dual=is_return_dual, main_model_primary=main_model_primary)
+        
+    secondary_sources = seperator.seperate()
+
+    if type(secondary_sources) is dict and not is_source_load and not is_pre_proc_model:
+        return gather_sources(secondary_model.primary_model_primary_stem, secondary_stem(secondary_model.primary_model_primary_stem), secondary_sources)
+    else:
+        return secondary_sources
+    
+def process_chain_model(secondary_model: ModelData, 
+                        process_data, 
+                        vocal_stem_path, 
+                        master_vocal_source, 
+                        master_inst_source=None):
+    
+    process_iteration = process_data['process_iteration']
+    process_iteration()
+    
+    if secondary_model.bv_model_rebalance:
+        vocal_source = spec_utils.reduce_mix_bv(master_inst_source, master_vocal_source, reduction_rate=secondary_model.bv_model_rebalance)
+    else:
+        vocal_source = master_vocal_source
+    
+    vocal_stem_path = [vocal_source, os.path.splitext(os.path.basename(vocal_stem_path))[0]]
+
+    if secondary_model.process_method == VR_ARCH_TYPE:
+        seperator = SeperateVR(secondary_model, process_data, vocal_stem_path=vocal_stem_path, master_inst_source=master_inst_source, master_vocal_source=master_vocal_source)
+    if secondary_model.process_method == MDX_ARCH_TYPE:
+        if secondary_model.is_mdx_c:
+            seperator = SeperateMDXC(secondary_model, process_data, vocal_stem_path=vocal_stem_path, master_inst_source=master_inst_source, master_vocal_source=master_vocal_source)
+        else:
+            seperator = SeperateMDX(secondary_model, process_data, vocal_stem_path=vocal_stem_path, master_inst_source=master_inst_source, master_vocal_source=master_vocal_source)
+    if secondary_model.process_method == DEMUCS_ARCH_TYPE:
+        seperator = SeperateDemucs(secondary_model, process_data, vocal_stem_path=vocal_stem_path, master_inst_source=master_inst_source, master_vocal_source=master_vocal_source)
+        
+    secondary_sources = seperator.seperate()
+    
+    if type(secondary_sources) is dict:
+        return secondary_sources
+    else:
+        return None
+    
+def gather_sources(primary_stem_name, secondary_stem_name, secondary_sources: dict):
+    
+    source_primary = False
+    source_secondary = False
+
+    for key, value in secondary_sources.items():
+        if key in primary_stem_name:
+            source_primary = value
+        if key in secondary_stem_name:
+            source_secondary = value
+
+    return source_primary, source_secondary
+        
+def prepare_mix(mix):
+    
+    audio_path = mix
+
+    if not isinstance(mix, np.ndarray):
+        mix, sr = librosa.load(mix, mono=False, sr=44100)
+    else:
+        mix = mix.T
+
+    if isinstance(audio_path, str):
+        if not np.any(mix) and audio_path.endswith('.mp3'):
+            mix = rerun_mp3(audio_path)
+
+    if mix.ndim == 1:
+        mix = np.asfortranarray([mix,mix])
+
+    return mix
+
+def rerun_mp3(audio_file, sample_rate=44100):
+
+    with audioread.audio_open(audio_file) as f:
+        track_length = int(f.duration)
+
+    return librosa.load(audio_file, duration=track_length, mono=False, sr=sample_rate)[0]
+
+def save_format(audio_path, save_format, mp3_bit_set):
+    
+    if not save_format == WAV:
+        
+        if OPERATING_SYSTEM == 'Darwin':
+            FFMPEG_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'ffmpeg')
+            pydub.AudioSegment.converter = FFMPEG_PATH
+        
+        musfile = pydub.AudioSegment.from_wav(audio_path)
+        
+        if save_format == FLAC:
+            audio_path_flac = audio_path.replace(".wav", ".flac")
+            musfile.export(audio_path_flac, format="flac")  
+        
+        if save_format == MP3:
+            audio_path_mp3 = audio_path.replace(".wav", ".mp3")
+            try:
+                musfile.export(audio_path_mp3, format="mp3", bitrate=mp3_bit_set, codec="libmp3lame")
+            except Exception as e:
+                print(e)
+                musfile.export(audio_path_mp3, format="mp3", bitrate=mp3_bit_set)
+        
+        try:
+            os.remove(audio_path)
+        except Exception as e:
+            print(e)
+            
+def pitch_shift(mix):
+    new_sr = 31183
+
+    # Resample audio file
+    resampled_audio = signal.resample_poly(mix, new_sr, 44100)
+    
+    return resampled_audio
+
+def list_to_dictionary(lst):
+    dictionary = {item: index for index, item in enumerate(lst)}
+    return dictionary
+
+def vr_denoiser(X, device, hop_length=1024, n_fft=2048, cropsize=256, is_deverber=False, model_path=None):
+    batchsize = 4
+
+    if is_deverber:
+        nout, nout_lstm = 64, 128
+        mp = ModelParameters(os.path.join('lib_v5', 'vr_network', 'modelparams', '4band_v3.json'))
+        n_fft = mp.param['bins'] * 2
+    else:
+        mp = None
+        hop_length=1024
+        nout, nout_lstm = 16, 128
+    
+    model = nets_new.CascadedNet(n_fft, nout=nout, nout_lstm=nout_lstm)
+    model.load_state_dict(torch.load(model_path, map_location=cpu))
+    model.to(device)
+
+    if mp is None:
+        X_spec = spec_utils.wave_to_spectrogram_old(X, hop_length, n_fft)
+    else:
+        X_spec = loading_mix(X.T, mp)
+   
+    #PreProcess
+    X_mag = np.abs(X_spec)
+    X_phase = np.angle(X_spec)
+
+    #Sep
+    n_frame = X_mag.shape[2]
+    pad_l, pad_r, roi_size = spec_utils.make_padding(n_frame, cropsize, model.offset)
+    X_mag_pad = np.pad(X_mag, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
+    X_mag_pad /= X_mag_pad.max()
+
+    X_dataset = []
+    patches = (X_mag_pad.shape[2] - 2 * model.offset) // roi_size
+    for i in range(patches):
+        start = i * roi_size
+        X_mag_crop = X_mag_pad[:, :, start:start + cropsize]
+        X_dataset.append(X_mag_crop)
+
+    X_dataset = np.asarray(X_dataset)
+
+    model.eval()
+    
+    with torch.no_grad():
+        mask = []
+        # To reduce the overhead, dataloader is not used.
+        for i in range(0, patches, batchsize):
+            X_batch = X_dataset[i: i + batchsize]
+            X_batch = torch.from_numpy(X_batch).to(device)
+
+            pred = model.predict_mask(X_batch)
+
+            pred = pred.detach().cpu().numpy()
+            pred = np.concatenate(pred, axis=2)
+            mask.append(pred)
+
+        mask = np.concatenate(mask, axis=2)
+    
+    mask = mask[:, :, :n_frame]
+
+    #Post Proc
+    if is_deverber:
+        v_spec = mask * X_mag * np.exp(1.j * X_phase)
+        y_spec = (1 - mask) * X_mag * np.exp(1.j * X_phase)
+    else:
+        v_spec = (1 - mask) * X_mag * np.exp(1.j * X_phase)
+
+    if mp is None:
+        wave = spec_utils.spectrogram_to_wave_old(v_spec, hop_length=1024)
+    else:
+        wave = spec_utils.cmb_spectrogram_to_wave(v_spec, mp, is_v51_model=True).T
+        
+    wave = spec_utils.match_array_shapes(wave, X)
+
+    if is_deverber:
+        wave_2 = spec_utils.cmb_spectrogram_to_wave(y_spec, mp, is_v51_model=True).T
+        wave_2 = spec_utils.match_array_shapes(wave_2, X)
+        return wave, wave_2
+    else:
+        return wave
+
+def loading_mix(X, mp):
+
+    X_wave, X_spec_s = {}, {}
+    
+    bands_n = len(mp.param['band'])
+    
+    for d in range(bands_n, 0, -1):        
+        bp = mp.param['band'][d]
+    
+        if OPERATING_SYSTEM == 'Darwin':
+            wav_resolution = 'polyphase' if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else bp['res_type']
+        else:
+            wav_resolution = 'polyphase'#bp['res_type']
+    
+        if d == bands_n: # high-end band
+            X_wave[d] = X
+
+        else: # lower bands
+            X_wave[d] = librosa.resample(X_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=wav_resolution)
+            
+        X_spec_s[d] = spec_utils.wave_to_spectrogram(X_wave[d], bp['hl'], bp['n_fft'], mp, band=d, is_v51_model=True)
+        
+        # if d == bands_n and is_high_end_process:
+        #     input_high_end_h = (bp['n_fft']//2 - bp['crop_stop']) + (mp.param['pre_filter_stop'] - mp.param['pre_filter_start'])
+        #     input_high_end = X_spec_s[d][:, bp['n_fft']//2-input_high_end_h:bp['n_fft']//2, :]
+
+    X_spec = spec_utils.combine_spectrograms(X_spec_s, mp)
+    
+    del X_wave, X_spec_s
+
+    return X_spec