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