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 = 1258 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)