separate.py

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 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 *
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 traceback
import lib_v5.mdxnet as MdxnetSet

if TYPE_CHECKING:
    from UVR import ModelData

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):
        
        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']
        self.audio_file = process_data['audio_file']
        self.audio_file_base = process_data['audio_file_base']
        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.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 = model_data.primary_stem #
        self.secondary_stem = model_data.secondary_stem #
        self.is_invert_spec = model_data.is_invert_spec #
        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

        if not model_data.process_method == DEMUCS_ARCH_TYPE:
            if process_data['is_ensemble_master'] and not self.is_4_stem_ensemble:
                if not model_data.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
            
            if self.is_secondary_model and not process_data['is_ensemble_master']:
                if not self.primary_model_primary_stem == self.primary_stem and not main_model_primary_stem_4_stem:
                    self.is_primary_stem_only, self.is_secondary_stem_only = self.is_secondary_stem_only, self.is_primary_stem_only
                    
            if main_model_primary_stem_4_stem:
                self.is_primary_stem_only = True if main_model_primary_stem_4_stem == self.primary_stem else False
                self.is_secondary_stem_only = True if not main_model_primary_stem_4_stem == self.primary_stem else False

            if self.is_pre_proc_model:
                self.is_primary_stem_only = True if self.primary_stem == INST_STEM else False
                self.is_secondary_stem_only = True if self.secondary_stem == INST_STEM else False

        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.mdx_batch_size = model_data.mdx_batch_size
            self.compensate = model_data.compensate
            self.dim_f, self.dim_t = model_data.mdx_dim_f_set, 2**model_data.mdx_dim_t_set
            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 self.is_gpu_conversion >= 0 and torch.cuda.is_available():
                self.device, self.run_type = torch.device('cuda:0'), ['CUDAExecutionProvider']
            else:
                self.device, self.run_type = torch.device('cpu'), ['CPUExecutionProvider']

        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.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
            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
            
            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 = STEM_PAIR_MAPPER[self.primary_stem]
            
            if self.is_chunk_demucs:
                self.chunks_demucs = model_data.chunks_demucs
                self.margin_demucs = model_data.margin_demucs
            else:
                self.chunks_demucs = 0
                self.margin_demucs = 44100
                
            self.shifts = model_data.shifts
            self.is_split_mode = model_data.is_split_mode if not self.demucs_version == DEMUCS_V4 else True
            self.overlap = model_data.overlap
            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.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.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 start_inference_console_write(self):
        
        if self.is_secondary_model and not self.is_pre_proc_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))
        
    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:
            self.write_to_console(INFERENCE_STEP_1_SEC)
        elif self.is_pre_proc_model:
            self.write_to_console(INFERENCE_STEP_1_PRE)
        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, is_4_stem_demucs=False):
        
        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)

        if not is_4_stem_demucs:
            primary_stem, secondary_stem = gather_sources(self.primary_stem, self.secondary_stem, self.primary_sources)
            
            return primary_stem, secondary_stem
            
    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 write_audio(self, stem_path, stem_source, samplerate, secondary_model_source=None, model_scale=None):
                
        if not self.is_secondary_model:
            if self.is_secondary_model_activated:
                if 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)
            
            sf.write(stem_path, stem_source, samplerate, subtype=self.wav_type_set)
            save_format(stem_path, self.save_format, self.mp3_bit_set) if not self.is_ensemble_mode else None
            
            self.write_to_console(DONE, base_text='')
            self.set_progress_bar(0.95)

    def run_mixer(self, mix, sources):
        try:
            if self.is_mixer_mode and len(sources) == 4:
                mixer = MdxnetSet.Mixer(self.device, self.mixer_path).eval()
                with torch.no_grad():
                    mix = torch.tensor(mix, dtype=torch.float32)
                    sources_ = torch.tensor(sources).detach()
                    x = torch.cat([sources_, mix.unsqueeze(0)], 0)
                    sources_ = mixer(x)
                final_source = np.array(sources_)
            else:
                final_source = sources
        except Exception as e:
            error_name = f'{type(e).__name__}'
            traceback_text = ''.join(traceback.format_tb(e.__traceback__))
            message = f'{error_name}: "{e}"\n{traceback_text}"'
            print('Mixer Failed: ', message)
            final_source = sources
            
        return final_source

class SeperateMDX(SeperateAttributes):        

    def seperate(self):
        samplerate = 44100
          
        if self.primary_model_name == self.model_basename and self.primary_sources:
            self.primary_source, self.secondary_source = 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:
                ort_ = ort.InferenceSession(self.model_path, providers=self.run_type)
                self.model_run = lambda spek:ort_.run(None, {'input': spek.cpu().numpy()})[0]

            self.initialize_model_settings()
            self.running_inference_console_write()
            mdx_net_cut = True if self.primary_stem in MDX_NET_FREQ_CUT else False
            mix, raw_mix, samplerate = prepare_mix(self.audio_file, self.chunks, self.margin, mdx_net_cut=mdx_net_cut)
            source = self.demix_base(mix, is_ckpt=self.is_mdx_ckpt)[0]
            self.write_to_console(DONE, base_text='')            

        if self.is_secondary_model_activated:
            if 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_secondary_stem_only:
            self.write_to_console(f'{SAVING_STEM[0]}{self.primary_stem}{SAVING_STEM[1]}') if not self.is_secondary_model else None
            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 = spec_utils.normalize(source, self.is_normalization).T
            self.primary_source_map = {self.primary_stem: self.primary_source}
            self.write_audio(primary_stem_path, self.primary_source, samplerate, self.secondary_source_primary)

        if not self.is_primary_stem_only:
            self.write_to_console(f'{SAVING_STEM[0]}{self.secondary_stem}{SAVING_STEM[1]}') if not self.is_secondary_model else None
            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_base(raw_mix, is_match_mix=True)[0] if mdx_net_cut else raw_mix
                self.secondary_source, raw_mix = spec_utils.normalize_two_stem(source*self.compensate, raw_mix, self.is_normalization)
            
                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.secondary_stem: self.secondary_source}
            self.write_audio(secondary_stem_path, self.secondary_source, samplerate, self.secondary_source_secondary)

        torch.cuda.empty_cache()
        secondary_sources = {**self.primary_source_map, **self.secondary_source_map}

        self.cache_source(secondary_sources)

        if self.is_secondary_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.dim_t-1)
        self.window = torch.hann_window(window_length=self.n_fft, periodic=False).to(self.device)
        self.freq_pad = torch.zeros([1, self.dim_c, self.n_bins-self.dim_f, self.dim_t]).to(self.device)
        self.gen_size = self.chunk_size-2*self.trim

    def initialize_mix(self, mix, is_ckpt=False):
        if is_ckpt:
            pad = self.gen_size + self.trim - ((mix.shape[-1]) % self.gen_size)
            mixture = np.concatenate((np.zeros((2, self.trim), dtype='float32'),mix, np.zeros((2, pad), dtype='float32')), 1)
            num_chunks = mixture.shape[-1] // self.gen_size
            mix_waves = [mixture[:, i * self.gen_size: i * self.gen_size + self.chunk_size] for i in range(num_chunks)]
        else:
            mix_waves = []
            n_sample = mix.shape[1]
            pad = self.gen_size - n_sample%self.gen_size
            mix_p = np.concatenate((np.zeros((2,self.trim)), mix, np.zeros((2,pad)), np.zeros((2,self.trim))), 1)
            i = 0
            while i < n_sample + pad:
                waves = np.array(mix_p[:, i:i+self.chunk_size])
                mix_waves.append(waves)
                i += self.gen_size
                
        mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(self.device)

        return mix_waves, pad
    
    def demix_base(self, mix, is_ckpt=False, is_match_mix=False):
        chunked_sources = []
        for slice in mix:
            sources = []
            tar_waves_ = []
            mix_p = mix[slice]
            mix_waves, pad = self.initialize_mix(mix_p, is_ckpt=is_ckpt)
            mix_waves = mix_waves.split(self.mdx_batch_size)
            pad = mix_p.shape[-1] if is_ckpt else -pad
            with torch.no_grad():
                for mix_wave in mix_waves:
                    self.running_inference_progress_bar(len(mix)*len(mix_waves), is_match_mix=is_match_mix)
                    tar_waves = self.run_model(mix_wave, is_ckpt=is_ckpt, is_match_mix=is_match_mix)
                    tar_waves_.append(tar_waves)
                tar_waves_ = np.vstack(tar_waves_)[:, :, self.trim:-self.trim] if is_ckpt else tar_waves_
                tar_waves = np.concatenate(tar_waves_, axis=-1)[:, :pad]
                start = 0 if slice == 0 else self.margin
                end = None if slice == list(mix.keys())[::-1][0] or self.margin == 0 else -self.margin
                sources.append(tar_waves[:,start:end]*(1/self.adjust))
            chunked_sources.append(sources)
        sources = np.concatenate(chunked_sources, axis=-1)
        
        return sources

    def run_model(self, mix, is_ckpt=False, 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)

        if is_ckpt:
            return self.istft(spec_pred).cpu().detach().numpy()
        else: 
            return self.istft(torch.tensor(spec_pred).to(self.device)).to(cpu)[:,:,self.trim:-self.trim].transpose(0,1).reshape(2, -1).numpy()
    
    def stft(self, x):
        x = x.reshape([-1, self.chunk_size])
        x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True,return_complex=True)
        x=torch.view_as_real(x)
        x = x.permute([0,3,1,2])
        x = x.reshape([-1,2,2,self.n_bins,self.dim_t]).reshape([-1,self.dim_c,self.n_bins,self.dim_t])
        return x[:,:,:self.dim_f]

    def istft(self, x, freq_pad=None):
        freq_pad = self.freq_pad.repeat([x.shape[0],1,1,1]) if freq_pad is None else freq_pad
        x = torch.cat([x, freq_pad], -2)
        x = x.reshape([-1,2,2,self.n_bins,self.dim_t]).reshape([-1,2,self.n_bins,self.dim_t])
        x = x.permute([0,2,3,1])
        x=x.contiguous()
        x=torch.view_as_complex(x)
        x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
        return x.reshape([-1,2,self.chunk_size])

class SeperateDemucs(SeperateAttributes):        

    def seperate(self):

        samplerate = 44100
        source = None
        model_scale = None
        stem_source = None
        stem_source_secondary = None
        inst_mix = None
        inst_raw_mix = None
        raw_mix = None
        inst_source = None
        is_no_write = False
        is_no_piano_guitar = False

        if self.primary_model_name == self.model_basename and type(self.primary_sources) is dict and not self.pre_proc_model:
            self.primary_source, self.secondary_source = self.load_cached_sources()
        elif 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(is_4_stem_demucs=True)
        else:
            self.start_inference_console_write()

            if self.is_gpu_conversion >= 0:
                self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') 
            else:
                self.device = torch.device('cpu')
            
            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, inst_raw_mix, inst_samplerate = prepare_mix(mix_no_voc[INST_STEM], self.chunks_demucs, self.margin_demucs)
                    self.process_iteration()
                    self.running_inference_console_write(is_no_write=is_no_write)
                    inst_source = self.demix_demucs(inst_mix)
                    inst_source = self.run_mixer(inst_raw_mix, inst_source)
                    self.process_iteration()

            self.running_inference_console_write(is_no_write=is_no_write) if not self.pre_proc_model else None
            mix, raw_mix, samplerate = prepare_mix(self.audio_file, self.chunks_demucs, self.margin_demucs)
            
            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)
                source = self.run_mixer(raw_mix, source)
            
            self.write_to_console(DONE, base_text='')
            
            del self.demucs
            torch.cuda.empty_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 (self.demucs_stems == ALL_STEMS and not self.process_data['is_ensemble_master']) or self.is_4_stem_ensemble:
            self.cache_source(source)
            
            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_4_stem_demucs=True)
                        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]
                            stem_source_secondary = spec_utils.normalize(stem_source_secondary, self.is_normalization).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
                self.write_to_console(f'{SAVING_STEM[0]}{stem_name}{SAVING_STEM[1]}') if not self.is_secondary_model else None
                stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({stem_name}).wav')
                stem_source = spec_utils.normalize(source[stem_value], self.is_normalization).T
                self.write_audio(stem_path, stem_source, samplerate, secondary_model_source=stem_source_secondary, model_scale=model_scale)

            if self.is_secondary_model:    
                return source
        else:
            if self.is_secondary_model_activated:
                if 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_secondary_stem_only:
                self.write_to_console(f'{SAVING_STEM[0]}{self.primary_stem}{SAVING_STEM[1]}') if not self.is_secondary_model else None
                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 = spec_utils.normalize(source[self.demucs_source_map[self.primary_stem]], self.is_normalization).T
                self.primary_source_map = {self.primary_stem: self.primary_source}
                self.write_audio(primary_stem_path, self.primary_source, samplerate, self.secondary_source_primary)

            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
                    self.write_to_console(f'{SAVING_STEM[0]}{sec_stem_name}{SAVING_STEM[1]}') if not self.is_secondary_model 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 = spec_utils.normalize(secondary_source, self.is_normalization).T
                        else:
                            if not isinstance(raw_mixture, np.ndarray):
                                raw_mixture = prepare_mix(self.audio_file, self.chunks_demucs, self.margin_demucs, is_missing_mix=True)
       
                            secondary_source, raw_mixture = spec_utils.normalize_two_stem(source[self.demucs_source_map[self.primary_stem]], raw_mixture, self.is_normalization)
                            
                            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_map = {self.secondary_stem: self.secondary_source}

                    self.write_audio(secondary_stem_path, secondary_source, samplerate, secondary_source_secondary)

                secondary_save(self.secondary_stem, source, raw_mixture=raw_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_raw_mix, is_inst_mixture=True)

            secondary_sources = {**self.primary_source_map, **self.secondary_source_map}

            self.cache_source(secondary_sources)
            
            if self.is_secondary_model:    
                return secondary_sources
    
    def demix_demucs(self, mix):
        processed = {}

        set_progress_bar = None if self.is_chunk_demucs else self.set_progress_bar

        for nmix in mix:
            self.progress_value += 1
            self.set_progress_bar(0.1, (0.8/len(mix)*self.progress_value)) if self.is_chunk_demucs else None
            cmix = mix[nmix]
            cmix = torch.tensor(cmix, dtype=torch.float32)
            ref = cmix.mean(0)        
            cmix = (cmix - ref.mean()) / ref.std()
            mix_infer = cmix 
            
            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=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=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=set_progress_bar,
                                            device=self.device)[0]
            
            sources = (sources * ref.std() + ref.mean()).cpu().numpy()
            sources[[0,1]] = sources[[1,0]]
            start = 0 if nmix == 0 else self.margin_demucs
            end = None if nmix == list(mix.keys())[::-1][0] else -self.margin_demucs
            if self.margin_demucs == 0:
                end = None
            processed[nmix] = sources[:,:,start:end].copy()
            sources = list(processed.values())
        sources = np.concatenate(sources, axis=-1)
                        
        return sources

class SeperateVR(SeperateAttributes):        

    def seperate(self):
        if self.primary_model_name == self.model_basename and self.primary_sources:
            self.primary_source, self.secondary_source = self.load_cached_sources()
        else:
            self.start_inference_console_write()
            if self.is_gpu_conversion >= 0:
                if OPERATING_SYSTEM == 'Darwin':
                    device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')
                else:
                    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') 
            else:
                device = torch.device('cpu')

            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])
            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)
            self.write_to_console(DONE, base_text='')
            
        if self.is_secondary_model_activated:
            if 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_secondary_stem_only:
            self.write_to_console(f'{SAVING_STEM[0]}{self.primary_stem}{SAVING_STEM[1]}') if not self.is_secondary_model else None
            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 = spec_utils.normalize(self.spec_to_wav(y_spec), self.is_normalization).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.primary_stem: self.primary_source}
            
            self.write_audio(primary_stem_path, self.primary_source, 44100, self.secondary_source_primary)

        if not self.is_primary_stem_only:
            self.write_to_console(f'{SAVING_STEM[0]}{self.secondary_stem}{SAVING_STEM[1]}') if not self.is_secondary_model else None
            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)
                self.secondary_source = spec_utils.normalize(self.spec_to_wav(v_spec), self.is_normalization).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.secondary_stem: self.secondary_source}
            
            self.write_audio(secondary_stem_path, self.secondary_source, 44100, self.secondary_source_secondary)
            
        torch.cuda.empty_cache()
        secondary_sources = {**self.primary_source_map, **self.secondary_source_map}
        self.cache_source(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'])
        
        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(self.audio_file, bp['sr'], False, dtype=np.float32, res_type=wav_resolution)
                    
                if not np.any(X_wave[d]) and self.audio_file.endswith('.mp3'):
                    X_wave[d] = rerun_mp3(self.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_mt(X_wave[d], bp['hl'], bp['n_fft'], self.mp.param['mid_side'], 
                                                            self.mp.param['mid_side_b2'], self.mp.param['reverse'])
            
            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)
        
        del X_wave, X_spec_s

        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'):        
            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_)       
        else:
            wav = spec_utils.cmb_spectrogram_to_wave(spec, self.mp)
            
        return wav
   
def process_secondary_model(secondary_model: ModelData, process_data, main_model_primary_stem_4_stem=None, is_4_stem_demucs=False, main_process_method=None, is_pre_proc_model=False):
        
    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)
    if secondary_model.process_method == MDX_ARCH_TYPE:
        seperator = SeperateMDX(secondary_model, process_data, main_model_primary_stem_4_stem=main_model_primary_stem_4_stem, main_process_method=main_process_method)
    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)
        
    secondary_sources = seperator.seperate()

    if type(secondary_sources) is dict and not is_4_stem_demucs and not is_pre_proc_model:
        return gather_sources(secondary_model.primary_model_primary_stem, STEM_PAIR_MAPPER[secondary_model.primary_model_primary_stem], secondary_sources)
    else:
        return secondary_sources
    
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, chunk_set, margin_set, mdx_net_cut=False, is_missing_mix=False):

    audio_path = mix
    samplerate = 44100

    if not isinstance(mix, np.ndarray):
        mix, samplerate = librosa.load(mix, mono=False, sr=44100)
    else:
        mix = mix.T

    if not np.any(mix) and audio_path.endswith('.mp3'):
        mix = rerun_mp3(audio_path)

    if mix.ndim == 1:
        mix = np.asfortranarray([mix,mix])

    def get_segmented_mix(chunk_set=chunk_set):
        segmented_mix = {}
        
        samples = mix.shape[-1]
        margin = margin_set
        chunk_size = chunk_set*44100
        assert not margin == 0, 'margin cannot be zero!'
        
        if margin > chunk_size:
            margin = chunk_size
        if chunk_set == 0 or samples < chunk_size:
            chunk_size = samples
        
        counter = -1
        for skip in range(0, samples, chunk_size):
            counter+=1
            s_margin = 0 if counter == 0 else margin
            end = min(skip+chunk_size+margin, samples)
            start = skip-s_margin
            segmented_mix[skip] = mix[:,start:end].copy()
            if end == samples:
                break
            
        return segmented_mix

    if is_missing_mix:
        return mix
    else:
        segmented_mix = get_segmented_mix()
        raw_mix = get_segmented_mix(chunk_set=0) if mdx_net_cut else mix
        return segmented_mix, raw_mix, samplerate

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")
            musfile.export(audio_path_mp3, format="mp3", bitrate=mp3_bit_set)
        
        try:
            os.remove(audio_path)
        except Exception as e:
            print(e)