/
separate.py
1459 lines (1161 loc) · 72.3 KB
/
separate.py
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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: