mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-09-24 03:28:36 +02:00
931 lines
48 KiB
Python
931 lines
48 KiB
Python
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 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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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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if TYPE_CHECKING:
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from UVR import ModelData
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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, process_data: dict, main_model_primary_stem_4_stem=None, main_process_method=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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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.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.model_samplerate = model_data.model_samplerate
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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 = 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.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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if not model_data.process_method == DEMUCS_ARCH_TYPE:
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if process_data['is_ensemble_master'] and not self.is_4_stem_ensemble:
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if not model_data.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_secondary_model and not process_data['is_ensemble_master']:
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if not self.primary_model_primary_stem == self.primary_stem and not main_model_primary_stem_4_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 main_model_primary_stem_4_stem:
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self.is_primary_stem_only = True if main_model_primary_stem_4_stem == self.primary_stem else False
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self.is_secondary_stem_only = True if not main_model_primary_stem_4_stem == self.primary_stem else False
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if self.is_pre_proc_model:
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self.is_primary_stem_only = True if self.primary_stem == INST_STEM else False
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self.is_secondary_stem_only = True if self.secondary_stem == INST_STEM else False
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if model_data.process_method == MDX_ARCH_TYPE:
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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.compensate = model_data.compensate
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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.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.hop = 1024
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self.n_bins = self.n_fft//2+1
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self.chunk_size = self.hop * (self.dim_t-1)
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self.window = torch.hann_window(window_length=self.n_fft, periodic=False).to(cpu)
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self.dim_c = 4
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out_c = self.dim_c
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self.freq_pad = torch.zeros([1, out_c, self.n_bins-self.dim_f, self.dim_t]).to(cpu)
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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.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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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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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 = STEM_PAIR_MAPPER[self.primary_stem]
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if self.is_chunk_demucs:
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self.chunks_demucs = model_data.chunks_demucs
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self.margin_demucs = model_data.margin_demucs
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else:
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self.chunks_demucs = 0
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self.margin_demucs = 44100
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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.overlap = model_data.overlap
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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.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.crop_size = model_data.crop_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.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 start_inference(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(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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def running_inference(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:
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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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else:
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self.write_to_console(INFERENCE_STEP_1)
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def load_cached_sources(self, is_4_stem_demucs=False):
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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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if not is_4_stem_demucs:
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primary_stem, secondary_stem = gather_sources(self.primary_stem, self.secondary_stem, self.primary_sources)
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return primary_stem, secondary_stem
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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 write_audio(self, stem_path, stem_source, samplerate, 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:
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if 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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sf.write(stem_path, stem_source, samplerate, subtype=self.wav_type_set)
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save_format(stem_path, self.save_format, self.mp3_bit_set) if not self.is_ensemble_mode else None
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self.write_to_console(DONE, base_text='')
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self.set_progress_bar(0.95)
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class SeperateMDX(SeperateAttributes):
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def seperate(self):
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samplerate = 44100
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if self.primary_model_name == self.model_basename and self.primary_sources:
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self.primary_source, self.secondary_source = self.load_cached_sources()
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else:
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self.start_inference()
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if self.is_gpu_conversion >= 0:
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self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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run_type = ['CUDAExecutionProvider'] if torch.cuda.is_available() else ['CPUExecutionProvider']
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else:
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self.device = torch.device('cpu')
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run_type = ['CPUExecutionProvider']
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self.onnx_model = ort.InferenceSession(self.model_path, providers=run_type)
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self.running_inference()
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mdx_net_cut = True if self.primary_stem in MDX_NET_FREQ_CUT else False
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mix, raw_mix, samplerate = prepare_mix(self.audio_file, self.chunks, self.margin, mdx_net_cut=mdx_net_cut)
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source = self.demix_base(mix)
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self.write_to_console(DONE, base_text='')
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if self.is_secondary_model_activated:
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if self.secondary_model:
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self.secondary_source_primary, self.secondary_source_secondary = process_secondary_model(self.secondary_model, self.process_data, main_process_method=self.process_method)
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if not self.is_secondary_stem_only:
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self.write_to_console(f'{SAVING_STEM[0]}{self.primary_stem}{SAVING_STEM[1]}') if not self.is_secondary_model else None
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primary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({self.primary_stem}).wav')
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if not isinstance(self.primary_source, np.ndarray):
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self.primary_source = spec_utils.normalize(source[0], self.is_normalization).T
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self.primary_source_map = {self.primary_stem: self.primary_source}
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self.write_audio(primary_stem_path, self.primary_source, samplerate, self.secondary_source_primary)
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if not self.is_primary_stem_only:
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self.write_to_console(f'{SAVING_STEM[0]}{self.secondary_stem}{SAVING_STEM[1]}') if not self.is_secondary_model else None
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secondary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({self.secondary_stem}).wav')
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if not isinstance(self.secondary_source, np.ndarray):
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raw_mix = self.demix_base(raw_mix, is_match_mix=True)[0] if mdx_net_cut else raw_mix
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self.secondary_source, raw_mix = spec_utils.normalize_two_stem(source[0]*self.compensate, raw_mix, self.is_normalization)
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if self.is_invert_spec:
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self.secondary_source = spec_utils.invert_stem(raw_mix, self.secondary_source)
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else:
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self.secondary_source = (-self.secondary_source.T+raw_mix.T)
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self.secondary_source_map = {self.secondary_stem: self.secondary_source}
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self.write_audio(secondary_stem_path, self.secondary_source, samplerate, self.secondary_source_secondary)
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torch.cuda.empty_cache()
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secondary_sources = {**self.primary_source_map, **self.secondary_source_map}
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self.cache_source(secondary_sources)
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if self.is_secondary_model:
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return secondary_sources
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def demix_base(self, mix, is_match_mix=False):
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chunked_sources = []
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for slice in mix:
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self.progress_value += 1
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self.set_progress_bar(0.1, (0.8/len(mix)*self.progress_value)) if not is_match_mix else None
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cmix = mix[slice]
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sources = []
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mix_waves = []
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n_sample = cmix.shape[1]
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trim = self.n_fft//2
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gen_size = self.chunk_size-2*trim
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pad = gen_size - n_sample%gen_size
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mix_p = np.concatenate((np.zeros((2,trim)), cmix, np.zeros((2,pad)), np.zeros((2,trim))), 1)
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i = 0
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while i < n_sample + pad:
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waves = np.array(mix_p[:, i:i+self.chunk_size])
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mix_waves.append(waves)
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i += gen_size
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mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu)
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with torch.no_grad():
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_ort = self.onnx_model if not is_match_mix else None
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adjust = 1
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spek = self.stft(mix_waves)*adjust
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if not is_match_mix:
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if self.is_denoise:
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spec_pred = -_ort.run(None, {'input': -spek.cpu().numpy()})[0]*0.5+_ort.run(None, {'input': spek.cpu().numpy()})[0]*0.5
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else:
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spec_pred = _ort.run(None, {'input': spek.cpu().numpy()})[0]
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else:
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spec_pred = spek.cpu().numpy()
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tar_waves = self.istft(torch.tensor(spec_pred))#.cpu()
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tar_signal = tar_waves[:,:,trim:-trim].transpose(0,1).reshape(2, -1).numpy()[:, :-pad]
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start = 0 if slice == 0 else self.margin
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end = None if slice == list(mix.keys())[::-1][0] else -self.margin
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if self.margin == 0:
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end = None
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sources.append(tar_signal[:,start:end]*(1/adjust))
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chunked_sources.append(sources)
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sources = np.concatenate(chunked_sources, axis=-1)
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if not is_match_mix:
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del self.onnx_model
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return sources
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def stft(self, x):
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x = x.reshape([-1, self.chunk_size])
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x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
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x = x.permute([0,3,1,2])
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x = x.reshape([-1,2,2,self.n_bins,self.dim_t]).reshape([-1,self.dim_c,self.n_bins,self.dim_t])
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return x[:,:,:self.dim_f]
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def istft(self, x, freq_pad=None):
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freq_pad = self.freq_pad.repeat([x.shape[0],1,1,1]) if freq_pad is None else freq_pad
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x = torch.cat([x, freq_pad], -2)
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c = 2
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x = x.reshape([-1,c,2,self.n_bins,self.dim_t]).reshape([-1,2,self.n_bins,self.dim_t])
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x = x.permute([0,2,3,1])
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x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
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return x.reshape([-1,c,self.chunk_size])
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class SeperateDemucs(SeperateAttributes):
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def seperate(self):
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samplerate = 44100
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source = None
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model_scale = None
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stem_source = None
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stem_source_secondary = None
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inst_mix = None
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inst_raw_mix = None
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raw_mix = None
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inst_source = None
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is_no_write = False
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is_no_piano_guitar = False
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if self.primary_model_name == self.model_basename and type(self.primary_sources) is dict and not self.pre_proc_model:
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self.primary_source, self.secondary_source = self.load_cached_sources()
|
|
elif self.primary_model_name == self.model_basename and isinstance(self.primary_sources, np.ndarray) and not self.pre_proc_model:
|
|
source = self.primary_sources
|
|
self.load_cached_sources(is_4_stem_demucs=True)
|
|
else:
|
|
self.start_inference()
|
|
|
|
if self.is_gpu_conversion >= 0:
|
|
self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
|
|
else:
|
|
self.device = torch.device('cpu')
|
|
|
|
if self.demucs_version == DEMUCS_V1:
|
|
if str(self.model_path).endswith(".gz"):
|
|
self.model_path = gzip.open(self.model_path, "rb")
|
|
klass, args, kwargs, state = torch.load(self.model_path)
|
|
self.demucs = klass(*args, **kwargs)
|
|
self.demucs.to(self.device)
|
|
self.demucs.load_state_dict(state)
|
|
elif self.demucs_version == DEMUCS_V2:
|
|
self.demucs = auto_load_demucs_model_v2(self.demucs_source_list, self.model_path)
|
|
self.demucs.to(self.device)
|
|
self.demucs.load_state_dict(torch.load(self.model_path))
|
|
self.demucs.eval()
|
|
else:
|
|
self.demucs = HDemucs(sources=self.demucs_source_list)
|
|
self.demucs = _gm(name=os.path.splitext(os.path.basename(self.model_path))[0],
|
|
repo=Path(os.path.dirname(self.model_path)))
|
|
self.demucs = demucs_segments(self.segment, self.demucs)
|
|
self.demucs.to(self.device)
|
|
self.demucs.eval()
|
|
|
|
if self.pre_proc_model:
|
|
if self.primary_stem not in [VOCAL_STEM, INST_STEM]:
|
|
is_no_write = True
|
|
self.write_to_console(DONE, base_text='')
|
|
mix_no_voc = process_secondary_model(self.pre_proc_model, self.process_data, is_pre_proc_model=True)
|
|
inst_mix, inst_raw_mix, inst_samplerate = prepare_mix(mix_no_voc[INST_STEM], self.chunks_demucs, self.margin_demucs)
|
|
self.process_iteration()
|
|
self.running_inference(is_no_write=is_no_write)
|
|
inst_source = self.demix_demucs(inst_mix)
|
|
self.process_iteration()
|
|
|
|
self.running_inference(is_no_write=is_no_write) if not self.pre_proc_model else None
|
|
mix, raw_mix, samplerate = prepare_mix(self.audio_file, self.chunks_demucs, self.margin_demucs)
|
|
|
|
if self.primary_model_name == self.model_basename and isinstance(self.primary_sources, np.ndarray) and self.pre_proc_model:
|
|
source = self.primary_sources
|
|
else:
|
|
source = self.demix_demucs(mix)
|
|
|
|
self.write_to_console(DONE, base_text='')
|
|
|
|
del self.demucs
|
|
|
|
if isinstance(inst_source, np.ndarray):
|
|
source_reshape = spec_utils.reshape_sources(inst_source[self.demucs_source_map[VOCAL_STEM]], source[self.demucs_source_map[VOCAL_STEM]])
|
|
inst_source[self.demucs_source_map[VOCAL_STEM]] = source_reshape
|
|
source = inst_source
|
|
|
|
if isinstance(source, np.ndarray):
|
|
if len(source) == 2:
|
|
self.demucs_source_map = DEMUCS_2_SOURCE_MAPPER
|
|
else:
|
|
self.demucs_source_map = DEMUCS_6_SOURCE_MAPPER if len(source) == 6 else DEMUCS_4_SOURCE_MAPPER
|
|
if len(source) == 6 and self.process_data['is_ensemble_master'] or len(source) == 6 and self.is_secondary_model:
|
|
is_no_piano_guitar = True
|
|
six_stem_other_source = list(source)
|
|
six_stem_other_source = [i for n, i in enumerate(source) if n in [self.demucs_source_map[OTHER_STEM], self.demucs_source_map[GUITAR_STEM], self.demucs_source_map[PIANO_STEM]]]
|
|
other_source = np.zeros_like(six_stem_other_source[0])
|
|
for i in six_stem_other_source:
|
|
other_source += i
|
|
source_reshape = spec_utils.reshape_sources(source[self.demucs_source_map[OTHER_STEM]], other_source)
|
|
source[self.demucs_source_map[OTHER_STEM]] = source_reshape
|
|
|
|
if (self.demucs_stems == ALL_STEMS and not self.process_data['is_ensemble_master']) or self.is_4_stem_ensemble:
|
|
self.cache_source(source)
|
|
|
|
for stem_name, stem_value in self.demucs_source_map.items():
|
|
|
|
if self.is_secondary_model_activated and not self.is_secondary_model and not stem_value >= 4:
|
|
if self.secondary_model_4_stem[stem_value]:
|
|
model_scale = self.secondary_model_4_stem_scale[stem_value]
|
|
stem_source_secondary = process_secondary_model(self.secondary_model_4_stem[stem_value], self.process_data, main_model_primary_stem_4_stem=stem_name, is_4_stem_demucs=True)
|
|
if isinstance(stem_source_secondary, np.ndarray):
|
|
stem_source_secondary = stem_source_secondary[1 if self.secondary_model_4_stem[stem_value].demucs_stem_count == 2 else stem_value]
|
|
stem_source_secondary = spec_utils.normalize(stem_source_secondary, self.is_normalization).T
|
|
elif type(stem_source_secondary) is dict:
|
|
stem_source_secondary = stem_source_secondary[stem_name]
|
|
|
|
stem_source_secondary = None if stem_value >= 4 else stem_source_secondary
|
|
self.write_to_console(f'{SAVING_STEM[0]}{stem_name}{SAVING_STEM[1]}') if not self.is_secondary_model else None
|
|
stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({stem_name}).wav')
|
|
stem_source = spec_utils.normalize(source[stem_value], self.is_normalization).T
|
|
self.write_audio(stem_path, stem_source, samplerate, secondary_model_source=stem_source_secondary, model_scale=model_scale)
|
|
|
|
if self.is_secondary_model:
|
|
return source
|
|
else:
|
|
if self.is_secondary_model_activated:
|
|
if self.secondary_model:
|
|
self.secondary_source_primary, self.secondary_source_secondary = process_secondary_model(self.secondary_model, self.process_data, main_process_method=self.process_method)
|
|
|
|
if not self.is_secondary_stem_only:
|
|
self.write_to_console(f'{SAVING_STEM[0]}{self.primary_stem}{SAVING_STEM[1]}') if not self.is_secondary_model else None
|
|
primary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({self.primary_stem}).wav')
|
|
if not isinstance(self.primary_source, np.ndarray):
|
|
self.primary_source = spec_utils.normalize(source[self.demucs_source_map[self.primary_stem]], self.is_normalization).T
|
|
self.primary_source_map = {self.primary_stem: self.primary_source}
|
|
self.write_audio(primary_stem_path, self.primary_source, samplerate, self.secondary_source_primary)
|
|
|
|
if not self.is_primary_stem_only:
|
|
def secondary_save(sec_stem_name, source, raw_mixture=None, is_inst_mixture=False):
|
|
secondary_source = self.secondary_source if not is_inst_mixture else None
|
|
self.write_to_console(f'{SAVING_STEM[0]}{sec_stem_name}{SAVING_STEM[1]}') if not self.is_secondary_model else None
|
|
secondary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({sec_stem_name}).wav')
|
|
secondary_source_secondary = None
|
|
|
|
if not isinstance(secondary_source, np.ndarray):
|
|
if self.is_demucs_combine_stems:
|
|
source = list(source)
|
|
if is_inst_mixture:
|
|
source = [i for n, i in enumerate(source) if not n in [self.demucs_source_map[self.primary_stem], self.demucs_source_map[VOCAL_STEM]]]
|
|
else:
|
|
source.pop(self.demucs_source_map[self.primary_stem])
|
|
|
|
source = source[:len(source) - 2] if is_no_piano_guitar else source
|
|
secondary_source = np.zeros_like(source[0])
|
|
for i in source:
|
|
secondary_source += i
|
|
secondary_source = spec_utils.normalize(secondary_source, self.is_normalization).T
|
|
else:
|
|
if not isinstance(raw_mixture, np.ndarray):
|
|
raw_mixture = prepare_mix(self.audio_file, self.chunks_demucs, self.margin_demucs, is_missing_mix=True)
|
|
|
|
secondary_source, raw_mixture = spec_utils.normalize_two_stem(source[self.demucs_source_map[self.primary_stem]], raw_mixture, self.is_normalization)
|
|
|
|
if self.is_invert_spec:
|
|
secondary_source = spec_utils.invert_stem(raw_mixture, secondary_source)
|
|
else:
|
|
raw_mixture = spec_utils.reshape_sources(secondary_source, raw_mixture)
|
|
secondary_source = (-secondary_source.T+raw_mixture.T)
|
|
|
|
if not is_inst_mixture:
|
|
self.secondary_source = secondary_source
|
|
secondary_source_secondary = self.secondary_source_secondary
|
|
self.secondary_source_map = {self.secondary_stem: self.secondary_source}
|
|
|
|
self.write_audio(secondary_stem_path, secondary_source, samplerate, secondary_source_secondary)
|
|
|
|
secondary_save(self.secondary_stem, source, raw_mixture=raw_mix)
|
|
|
|
if self.is_demucs_pre_proc_model_inst_mix and self.pre_proc_model and not self.is_4_stem_ensemble:
|
|
secondary_save(f"{self.secondary_stem} {INST_STEM}", source, raw_mixture=inst_raw_mix, is_inst_mixture=True)
|
|
|
|
torch.cuda.empty_cache()
|
|
|
|
secondary_sources = {**self.primary_source_map, **self.secondary_source_map}
|
|
|
|
self.cache_source(secondary_sources)
|
|
|
|
if self.is_secondary_model:
|
|
return secondary_sources
|
|
|
|
def demix_demucs(self, mix):
|
|
processed = {}
|
|
|
|
set_progress_bar = None if self.is_chunk_demucs else self.set_progress_bar
|
|
|
|
for nmix in mix:
|
|
self.progress_value += 1
|
|
self.set_progress_bar(0.1, (0.8/len(mix)*self.progress_value)) if self.is_chunk_demucs else None
|
|
cmix = mix[nmix]
|
|
cmix = torch.tensor(cmix, dtype=torch.float32)
|
|
ref = cmix.mean(0)
|
|
cmix = (cmix - ref.mean()) / ref.std()
|
|
mix_infer = cmix
|
|
|
|
with torch.no_grad():
|
|
if self.demucs_version == DEMUCS_V1:
|
|
sources = apply_model_v1(self.demucs,
|
|
mix_infer.to(self.device),
|
|
self.shifts,
|
|
self.is_split_mode,
|
|
set_progress_bar=set_progress_bar)
|
|
elif self.demucs_version == DEMUCS_V2:
|
|
sources = apply_model_v2(self.demucs,
|
|
mix_infer.to(self.device),
|
|
self.shifts,
|
|
self.is_split_mode,
|
|
self.overlap,
|
|
set_progress_bar=set_progress_bar)
|
|
else:
|
|
sources = apply_model(self.demucs,
|
|
mix_infer[None],
|
|
self.shifts,
|
|
self.is_split_mode,
|
|
self.overlap,
|
|
static_shifts=1 if self.shifts == 0 else self.shifts,
|
|
set_progress_bar=set_progress_bar,
|
|
device=self.device)[0]
|
|
|
|
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
|
|
sources[[0,1]] = sources[[1,0]]
|
|
start = 0 if nmix == 0 else self.margin_demucs
|
|
end = None if nmix == list(mix.keys())[::-1][0] else -self.margin_demucs
|
|
if self.margin_demucs == 0:
|
|
end = None
|
|
processed[nmix] = sources[:,:,start:end].copy()
|
|
sources = list(processed.values())
|
|
sources = np.concatenate(sources, axis=-1)
|
|
|
|
return sources
|
|
|
|
class SeperateVR(SeperateAttributes):
|
|
|
|
def seperate(self):
|
|
|
|
if self.primary_model_name == self.model_basename and self.primary_sources:
|
|
self.primary_source, self.secondary_source = self.load_cached_sources()
|
|
else:
|
|
self.start_inference()
|
|
if self.is_gpu_conversion >= 0:
|
|
if OPERATING_SYSTEM == 'Darwin':
|
|
device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')
|
|
else:
|
|
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
|
|
else:
|
|
device = torch.device('cpu')
|
|
|
|
nn_arch_sizes = [
|
|
31191, # default
|
|
33966, 56817, 218409, 123821, 123812, 129605, 537238, 537227]
|
|
vr_5_1_models = [56817, 218409]
|
|
|
|
model_size = math.ceil(os.stat(self.model_path).st_size / 1024)
|
|
nn_architecture = min(nn_arch_sizes, key=lambda x:abs(x-model_size))
|
|
|
|
if nn_architecture in vr_5_1_models:
|
|
model = nets_new.CascadedNet(self.mp.param['bins'] * 2, nn_architecture)
|
|
inference = self.inference_vr_new
|
|
else:
|
|
model = nets.determine_model_capacity(self.mp.param['bins'] * 2, nn_architecture)
|
|
inference = self.inference_vr
|
|
|
|
model.load_state_dict(torch.load(self.model_path, map_location=device))
|
|
model.to(device)
|
|
|
|
self.running_inference()
|
|
|
|
y_spec, v_spec = inference(self.loading_mix(), device, model, self.aggressiveness)
|
|
self.write_to_console(DONE, base_text='')
|
|
|
|
del model
|
|
|
|
if self.is_secondary_model_activated:
|
|
if self.secondary_model:
|
|
self.secondary_source_primary, self.secondary_source_secondary = process_secondary_model(self.secondary_model, self.process_data, main_process_method=self.process_method)
|
|
|
|
if not self.is_secondary_stem_only:
|
|
self.write_to_console(f'{SAVING_STEM[0]}{self.primary_stem}{SAVING_STEM[1]}') if not self.is_secondary_model else None
|
|
primary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({self.primary_stem}).wav')
|
|
if not isinstance(self.primary_source, np.ndarray):
|
|
self.primary_source = spec_utils.normalize(self.spec_to_wav(y_spec), self.is_normalization).T
|
|
if not self.model_samplerate == 44100:
|
|
self.primary_source = librosa.resample(self.primary_source.T, orig_sr=self.model_samplerate, target_sr=44100).T
|
|
|
|
self.primary_source_map = {self.primary_stem: self.primary_source}
|
|
|
|
self.write_audio(primary_stem_path, self.primary_source, 44100, self.secondary_source_primary)
|
|
|
|
if not self.is_primary_stem_only:
|
|
self.write_to_console(f'{SAVING_STEM[0]}{self.secondary_stem}{SAVING_STEM[1]}') if not self.is_secondary_model else None
|
|
secondary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({self.secondary_stem}).wav')
|
|
if not isinstance(self.secondary_source, np.ndarray):
|
|
self.secondary_source = self.spec_to_wav(v_spec)
|
|
self.secondary_source = spec_utils.normalize(self.spec_to_wav(v_spec), self.is_normalization).T
|
|
if not self.model_samplerate == 44100:
|
|
self.secondary_source = librosa.resample(self.secondary_source.T, orig_sr=self.model_samplerate, target_sr=44100).T
|
|
|
|
self.secondary_source_map = {self.secondary_stem: self.secondary_source}
|
|
|
|
self.write_audio(secondary_stem_path, self.secondary_source, 44100, self.secondary_source_secondary)
|
|
|
|
torch.cuda.empty_cache()
|
|
|
|
secondary_sources = {**self.primary_source_map, **self.secondary_source_map}
|
|
self.cache_source(secondary_sources)
|
|
|
|
if self.is_secondary_model:
|
|
return secondary_sources
|
|
|
|
def loading_mix(self):
|
|
|
|
X_wave, X_spec_s = {}, {}
|
|
|
|
bands_n = len(self.mp.param['band'])
|
|
|
|
for d in range(bands_n, 0, -1):
|
|
bp = self.mp.param['band'][d]
|
|
|
|
if OPERATING_SYSTEM == 'Darwin':
|
|
wav_resolution = 'polyphase' if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else bp['res_type']
|
|
else:
|
|
wav_resolution = bp['res_type']
|
|
|
|
if d == bands_n: # high-end band
|
|
X_wave[d], _ = librosa.load(
|
|
self.audio_file, bp['sr'], False, dtype=np.float32, res_type=wav_resolution)
|
|
|
|
if X_wave[d].ndim == 1:
|
|
X_wave[d] = np.asarray([X_wave[d], X_wave[d]])
|
|
else: # lower bands
|
|
X_wave[d] = librosa.resample(X_wave[d+1], self.mp.param['band'][d+1]['sr'], bp['sr'], res_type=wav_resolution)
|
|
|
|
X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(X_wave[d], bp['hl'], bp['n_fft'], self.mp.param['mid_side'],
|
|
self.mp.param['mid_side_b2'], self.mp.param['reverse'])
|
|
|
|
if d == bands_n and self.high_end_process != 'none':
|
|
self.input_high_end_h = (bp['n_fft']//2 - bp['crop_stop']) + (self.mp.param['pre_filter_stop'] - self.mp.param['pre_filter_start'])
|
|
self.input_high_end = X_spec_s[d][:, bp['n_fft']//2-self.input_high_end_h:bp['n_fft']//2, :]
|
|
|
|
X_spec = spec_utils.combine_spectrograms(X_spec_s, self.mp)
|
|
|
|
del X_wave, X_spec_s
|
|
|
|
return X_spec
|
|
|
|
def inference_vr(self, X_spec, device, model, aggressiveness):
|
|
|
|
def _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness):
|
|
model.eval()
|
|
|
|
total_iterations = sum([n_window]) if not self.is_tta else sum([n_window])*2
|
|
|
|
with torch.no_grad():
|
|
preds = []
|
|
|
|
for i in range(n_window):
|
|
self.progress_value +=1
|
|
self.set_progress_bar(0.1, 0.8/total_iterations*self.progress_value)
|
|
start = i * roi_size
|
|
X_mag_window = X_mag_pad[None, :, :, start:start + self.window_size]
|
|
X_mag_window = torch.from_numpy(X_mag_window).to(device)
|
|
pred = model.predict(X_mag_window, aggressiveness)
|
|
pred = pred.detach().cpu().numpy()
|
|
preds.append(pred[0])
|
|
|
|
pred = np.concatenate(preds, axis=2)
|
|
return pred
|
|
|
|
X_mag, X_phase = spec_utils.preprocess(X_spec)
|
|
coef = X_mag.max()
|
|
X_mag_pre = X_mag / coef
|
|
n_frame = X_mag_pre.shape[2]
|
|
pad_l, pad_r, roi_size = spec_utils.make_padding(n_frame, self.window_size, model.offset)
|
|
n_window = int(np.ceil(n_frame / roi_size))
|
|
X_mag_pad = np.pad(
|
|
X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
|
|
pred = _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness)
|
|
pred = pred[:, :, :n_frame]
|
|
|
|
if self.is_tta:
|
|
pad_l += roi_size // 2
|
|
pad_r += roi_size // 2
|
|
n_window += 1
|
|
X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
|
|
pred_tta = _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness)
|
|
pred_tta = pred_tta[:, :, roi_size // 2:]
|
|
pred_tta = pred_tta[:, :, :n_frame]
|
|
pred, X_mag, X_phase = (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.j * X_phase)
|
|
else:
|
|
pred, X_mag, X_phase = pred * coef, X_mag, np.exp(1.j * X_phase)
|
|
|
|
if self.is_post_process:
|
|
pred_inv = np.clip(X_mag - pred, 0, np.inf)
|
|
pred = spec_utils.mask_silence(pred, pred_inv, thres=self.post_process_threshold)
|
|
|
|
y_spec = pred * X_phase
|
|
v_spec = X_spec - y_spec
|
|
|
|
return y_spec, v_spec
|
|
|
|
def inference_vr_new(self, X_spec, device, model, aggressiveness):
|
|
|
|
def _execute(X_mag_pad, roi_size):
|
|
|
|
X_dataset = []
|
|
patches = (X_mag_pad.shape[2] - 2 * model.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_crop = X_mag_pad[:, :, start:start + self.crop_size]
|
|
X_dataset.append(X_mag_crop)
|
|
|
|
X_dataset = np.asarray(X_dataset)
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
mask = []
|
|
# To reduce the overhead, dataloader is not used.
|
|
for i in range(0, patches, 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 = 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)
|
|
|
|
return mask
|
|
|
|
def postprocess(mask, X_mag, X_phase, aggressiveness):
|
|
|
|
if self.primary_stem == VOCAL_STEM:
|
|
mask = (1.0 - spec_utils.adjust_aggr(mask, True, aggressiveness))
|
|
else:
|
|
mask = spec_utils.adjust_aggr(mask, False, aggressiveness)
|
|
|
|
if self.is_post_process:
|
|
mask = spec_utils.merge_artifacts(mask)
|
|
|
|
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.crop_size, 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()
|
|
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, aggressiveness)
|
|
|
|
return y_spec, v_spec
|
|
|
|
def spec_to_wav(self, spec):
|
|
|
|
if self.high_end_process.startswith('mirroring'):
|
|
input_high_end_ = spec_utils.mirroring(self.high_end_process, spec, self.input_high_end, self.mp)
|
|
wav = spec_utils.cmb_spectrogram_to_wave(spec, self.mp, self.input_high_end_h, input_high_end_)
|
|
else:
|
|
wav = spec_utils.cmb_spectrogram_to_wave(spec, self.mp)
|
|
|
|
return wav
|
|
|
|
def process_secondary_model(secondary_model: ModelData, process_data, main_model_primary_stem_4_stem=None, is_4_stem_demucs=False, main_process_method=None, is_pre_proc_model=False):
|
|
|
|
if not is_pre_proc_model:
|
|
process_iteration = process_data['process_iteration']
|
|
process_iteration()
|
|
|
|
if secondary_model.process_method == VR_ARCH_TYPE:
|
|
seperator = SeperateVR(secondary_model, process_data, main_model_primary_stem_4_stem=main_model_primary_stem_4_stem, main_process_method=main_process_method)
|
|
if secondary_model.process_method == MDX_ARCH_TYPE:
|
|
seperator = SeperateMDX(secondary_model, process_data, main_model_primary_stem_4_stem=main_model_primary_stem_4_stem, main_process_method=main_process_method)
|
|
if secondary_model.process_method == DEMUCS_ARCH_TYPE:
|
|
seperator = SeperateDemucs(secondary_model, process_data, main_model_primary_stem_4_stem=main_model_primary_stem_4_stem, main_process_method=main_process_method)
|
|
|
|
secondary_sources = seperator.seperate()
|
|
|
|
if type(secondary_sources) is dict and not is_4_stem_demucs and not is_pre_proc_model:
|
|
return gather_sources(secondary_model.primary_model_primary_stem, STEM_PAIR_MAPPER[secondary_model.primary_model_primary_stem], secondary_sources)
|
|
else:
|
|
return secondary_sources
|
|
|
|
def gather_sources(primary_stem_name, secondary_stem_name, secondary_sources: dict):
|
|
|
|
source_primary = False
|
|
source_secondary = False
|
|
|
|
for key, value in secondary_sources.items():
|
|
if key in primary_stem_name:
|
|
source_primary = value
|
|
if key in secondary_stem_name:
|
|
source_secondary = value
|
|
|
|
return source_primary, source_secondary
|
|
|
|
def prepare_mix(mix, chunk_set, margin_set, mdx_net_cut=False, is_missing_mix=False):
|
|
|
|
samplerate = 44100
|
|
|
|
if not isinstance(mix, np.ndarray):
|
|
mix, samplerate = librosa.load(mix, mono=False, sr=44100)
|
|
else:
|
|
mix = mix.T
|
|
|
|
if mix.ndim == 1:
|
|
mix = np.asfortranarray([mix,mix])
|
|
|
|
def get_segmented_mix(chunk_set=chunk_set):
|
|
segmented_mix = {}
|
|
|
|
samples = mix.shape[-1]
|
|
margin = margin_set
|
|
chunk_size = chunk_set*44100
|
|
assert not margin == 0, 'margin cannot be zero!'
|
|
if margin > chunk_size:
|
|
margin = chunk_size
|
|
if chunk_set == 0 or samples < chunk_size:
|
|
chunk_size = samples
|
|
|
|
counter = -1
|
|
for skip in range(0, samples, chunk_size):
|
|
counter+=1
|
|
s_margin = 0 if counter == 0 else margin
|
|
end = min(skip+chunk_size+margin, samples)
|
|
start = skip-s_margin
|
|
segmented_mix[skip] = mix[:,start:end].copy()
|
|
if end == samples:
|
|
break
|
|
|
|
return segmented_mix
|
|
|
|
if is_missing_mix:
|
|
return mix
|
|
else:
|
|
segmented_mix = get_segmented_mix()
|
|
raw_mix = get_segmented_mix(chunk_set=0) if mdx_net_cut else mix
|
|
return segmented_mix, raw_mix, samplerate
|
|
|
|
def save_format(audio_path, save_format, mp3_bit_set):
|
|
|
|
if not save_format == WAV:
|
|
|
|
if OPERATING_SYSTEM == 'Darwin':
|
|
FFMPEG_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'ffmpeg')
|
|
pydub.AudioSegment.converter = FFMPEG_PATH
|
|
|
|
musfile = pydub.AudioSegment.from_wav(audio_path)
|
|
|
|
if save_format == FLAC:
|
|
audio_path_flac = audio_path.replace(".wav", ".flac")
|
|
musfile.export(audio_path_flac, format="flac")
|
|
|
|
if save_format == MP3:
|
|
audio_path_mp3 = audio_path.replace(".wav", ".mp3")
|
|
musfile.export(audio_path_mp3, format="mp3", bitrate=mp3_bit_set)
|
|
|
|
try:
|
|
os.remove(audio_path)
|
|
except Exception as e:
|
|
print(e)
|