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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 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 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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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 traceback |
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import lib_v5.mdxnet as MdxnetSet |
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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.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 = 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_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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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.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.mdx_batch_size = model_data.mdx_batch_size |
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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.adjust = 1 |
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self.dim_c = 4 |
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self.hop = 1024 |
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if self.is_gpu_conversion >= 0 and torch.cuda.is_available(): |
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self.device, self.run_type = torch.device('cuda:0'), ['CUDAExecutionProvider'] |
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else: |
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self.device, self.run_type = torch.device('cpu'), ['CPUExecutionProvider'] |
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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.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_console_write(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_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: |
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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 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, 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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def run_mixer(self, mix, sources): |
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try: |
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if self.is_mixer_mode and len(sources) == 4: |
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mixer = MdxnetSet.Mixer(self.device, self.mixer_path).eval() |
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with torch.no_grad(): |
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mix = torch.tensor(mix, dtype=torch.float32) |
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sources_ = torch.tensor(sources).detach() |
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x = torch.cat([sources_, mix.unsqueeze(0)], 0) |
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sources_ = mixer(x) |
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final_source = np.array(sources_) |
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else: |
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final_source = sources |
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except Exception as e: |
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error_name = f'{type(e).__name__}' |
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traceback_text = ''.join(traceback.format_tb(e.__traceback__)) |
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message = f'{error_name}: "{e}"\n{traceback_text}"' |
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print('Mixer Failed: ', message) |
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final_source = sources |
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return final_source |
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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_console_write() |
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if self.is_mdx_ckpt: |
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model_params = torch.load(self.model_path, map_location=lambda storage, loc: storage)['hyper_parameters'] |
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self.dim_c, self.hop = model_params['dim_c'], model_params['hop_length'] |
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separator = MdxnetSet.ConvTDFNet(**model_params) |
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self.model_run = separator.load_from_checkpoint(self.model_path).to(self.device).eval() |
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else: |
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ort_ = ort.InferenceSession(self.model_path, providers=self.run_type) |
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self.model_run = lambda spek:ort_.run(None, {'input': spek.cpu().numpy()})[0] |
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self.initialize_model_settings() |
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self.running_inference_console_write() |
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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, is_ckpt=self.is_mdx_ckpt)[0] |
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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, 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*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 initialize_model_settings(self): |
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self.n_bins = self.n_fft//2+1 |
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self.trim = self.n_fft//2 |
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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(self.device) |
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self.freq_pad = torch.zeros([1, self.dim_c, self.n_bins-self.dim_f, self.dim_t]).to(self.device) |
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self.gen_size = self.chunk_size-2*self.trim |
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def initialize_mix(self, mix, is_ckpt=False): |
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if is_ckpt: |
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pad = self.gen_size + self.trim - ((mix.shape[-1]) % self.gen_size) |
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mixture = np.concatenate((np.zeros((2, self.trim), dtype='float32'),mix, np.zeros((2, pad), dtype='float32')), 1) |
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num_chunks = mixture.shape[-1] // self.gen_size |
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mix_waves = [mixture[:, i * self.gen_size: i * self.gen_size + self.chunk_size] for i in range(num_chunks)] |
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else: |
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mix_waves = [] |
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n_sample = mix.shape[1] |
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pad = self.gen_size - n_sample%self.gen_size |
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mix_p = np.concatenate((np.zeros((2,self.trim)), mix, np.zeros((2,pad)), np.zeros((2,self.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 += self.gen_size |
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mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(self.device) |
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return mix_waves, pad |
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def demix_base(self, mix, is_ckpt=False, is_match_mix=False): |
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chunked_sources = [] |
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for slice in mix: |
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sources = [] |
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tar_waves_ = [] |
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mix_p = mix[slice] |
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mix_waves, pad = self.initialize_mix(mix_p, is_ckpt=is_ckpt) |
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mix_waves = mix_waves.split(self.mdx_batch_size) |
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pad = mix_p.shape[-1] if is_ckpt else -pad |
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with torch.no_grad(): |
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for mix_wave in mix_waves: |
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self.running_inference_progress_bar(len(mix)*len(mix_waves), is_match_mix=is_match_mix) |
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tar_waves = self.run_model(mix_wave, is_ckpt=is_ckpt, is_match_mix=is_match_mix) |
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tar_waves_.append(tar_waves) |
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tar_waves_ = np.vstack(tar_waves_)[:, :, self.trim:-self.trim] if is_ckpt else tar_waves_ |
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tar_waves = np.concatenate(tar_waves_, axis=-1)[:, :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] or self.margin == 0 else -self.margin |
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sources.append(tar_waves[:,start:end]*(1/self.adjust)) |
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chunked_sources.append(sources) |
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sources = np.concatenate(chunked_sources, axis=-1) |
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return sources |
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def run_model(self, mix, is_ckpt=False, is_match_mix=False): |
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spek = self.stft(mix.to(self.device))*self.adjust |
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spek[:, :, :3, :] *= 0 |
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if is_match_mix: |
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spec_pred = spek.cpu().numpy() |
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else: |
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spec_pred = -self.model_run(-spek)*0.5+self.model_run(spek)*0.5 if self.is_denoise else self.model_run(spek) |
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if is_ckpt: |
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return self.istft(spec_pred).cpu().detach().numpy() |
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else: |
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return self.istft(torch.tensor(spec_pred).to(self.device)).to(cpu)[:,:,self.trim:-self.trim].transpose(0,1).reshape(2, -1).numpy() |
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def stft(self, x): |
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x = x.reshape([-1, self.chunk_size]) |
|
x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True,return_complex=True) |
|
x=torch.view_as_real(x) |
|
x = x.permute([0,3,1,2]) |
|
x = x.reshape([-1,2,2,self.n_bins,self.dim_t]).reshape([-1,self.dim_c,self.n_bins,self.dim_t]) |
|
return x[:,:,:self.dim_f] |
|
|
|
def istft(self, x, freq_pad=None): |
|
freq_pad = self.freq_pad.repeat([x.shape[0],1,1,1]) if freq_pad is None else freq_pad |
|
x = torch.cat([x, freq_pad], -2) |
|
x = x.reshape([-1,2,2,self.n_bins,self.dim_t]).reshape([-1,2,self.n_bins,self.dim_t]) |
|
x = x.permute([0,2,3,1]) |
|
x=x.contiguous() |
|
x=torch.view_as_complex(x) |
|
x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True) |
|
return x.reshape([-1,2,self.chunk_size]) |
|
|
|
class SeperateDemucs(SeperateAttributes): |
|
|
|
def seperate(self): |
|
|
|
samplerate = 44100 |
|
source = None |
|
model_scale = None |
|
stem_source = None |
|
stem_source_secondary = None |
|
inst_mix = None |
|
inst_raw_mix = None |
|
raw_mix = None |
|
inst_source = None |
|
is_no_write = False |
|
is_no_piano_guitar = False |
|
|
|
if self.primary_model_name == self.model_basename and type(self.primary_sources) is dict and not self.pre_proc_model: |
|
self.primary_source, self.secondary_source = self.load_cached_sources() |
|
elif self.primary_model_name == self.model_basename and isinstance(self.primary_sources, np.ndarray) and not self.pre_proc_model: |
|
source = self.primary_sources |
|
self.load_cached_sources(is_4_stem_demucs=True) |
|
else: |
|
self.start_inference_console_write() |
|
|
|
if self.is_gpu_conversion >= 0: |
|
self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') |
|
else: |
|
self.device = torch.device('cpu') |
|
|
|
if self.demucs_version == DEMUCS_V1: |
|
if str(self.model_path).endswith(".gz"): |
|
self.model_path = gzip.open(self.model_path, "rb") |
|
klass, args, kwargs, state = torch.load(self.model_path) |
|
self.demucs = klass(*args, **kwargs) |
|
self.demucs.to(self.device) |
|
self.demucs.load_state_dict(state) |
|
elif self.demucs_version == DEMUCS_V2: |
|
self.demucs = auto_load_demucs_model_v2(self.demucs_source_list, self.model_path) |
|
self.demucs.to(self.device) |
|
self.demucs.load_state_dict(torch.load(self.model_path)) |
|
self.demucs.eval() |
|
else: |
|
self.demucs = HDemucs(sources=self.demucs_source_list) |
|
self.demucs = _gm(name=os.path.splitext(os.path.basename(self.model_path))[0], |
|
repo=Path(os.path.dirname(self.model_path))) |
|
self.demucs = demucs_segments(self.segment, self.demucs) |
|
self.demucs.to(self.device) |
|
self.demucs.eval() |
|
|
|
if self.pre_proc_model: |
|
if self.primary_stem not in [VOCAL_STEM, INST_STEM]: |
|
is_no_write = True |
|
self.write_to_console(DONE, base_text='') |
|
mix_no_voc = process_secondary_model(self.pre_proc_model, self.process_data, is_pre_proc_model=True) |
|
inst_mix, inst_raw_mix, inst_samplerate = prepare_mix(mix_no_voc[INST_STEM], self.chunks_demucs, self.margin_demucs) |
|
self.process_iteration() |
|
self.running_inference_console_write(is_no_write=is_no_write) |
|
inst_source = self.demix_demucs(inst_mix) |
|
inst_source = self.run_mixer(inst_raw_mix, inst_source) |
|
self.process_iteration() |
|
|
|
self.running_inference_console_write(is_no_write=is_no_write) if not self.pre_proc_model else None |
|
mix, raw_mix, samplerate = prepare_mix(self.audio_file, self.chunks_demucs, self.margin_demucs) |
|
|
|
if self.primary_model_name == self.model_basename and isinstance(self.primary_sources, np.ndarray) and self.pre_proc_model: |
|
source = self.primary_sources |
|
else: |
|
source = self.demix_demucs(mix) |
|
source = self.run_mixer(raw_mix, source) |
|
|
|
self.write_to_console(DONE, base_text='') |
|
|
|
del self.demucs |
|
torch.cuda.empty_cache() |
|
|
|
if isinstance(inst_source, np.ndarray): |
|
source_reshape = spec_utils.reshape_sources(inst_source[self.demucs_source_map[VOCAL_STEM]], source[self.demucs_source_map[VOCAL_STEM]]) |
|
inst_source[self.demucs_source_map[VOCAL_STEM]] = source_reshape |
|
source = inst_source |
|
|
|
if isinstance(source, np.ndarray): |
|
if len(source) == 2: |
|
self.demucs_source_map = DEMUCS_2_SOURCE_MAPPER |
|
else: |
|
self.demucs_source_map = DEMUCS_6_SOURCE_MAPPER if len(source) == 6 else DEMUCS_4_SOURCE_MAPPER |
|
|
|
if len(source) == 6 and self.process_data['is_ensemble_master'] or len(source) == 6 and self.is_secondary_model: |
|
is_no_piano_guitar = True |
|
six_stem_other_source = list(source) |
|
six_stem_other_source = [i for n, i in enumerate(source) if n in [self.demucs_source_map[OTHER_STEM], self.demucs_source_map[GUITAR_STEM], self.demucs_source_map[PIANO_STEM]]] |
|
other_source = np.zeros_like(six_stem_other_source[0]) |
|
for i in six_stem_other_source: |
|
other_source += i |
|
source_reshape = spec_utils.reshape_sources(source[self.demucs_source_map[OTHER_STEM]], other_source) |
|
source[self.demucs_source_map[OTHER_STEM]] = source_reshape |
|
|
|
if (self.demucs_stems == ALL_STEMS and not self.process_data['is_ensemble_master']) or self.is_4_stem_ensemble: |
|
self.cache_source(source) |
|
|
|
for stem_name, stem_value in self.demucs_source_map.items(): |
|
if self.is_secondary_model_activated and not self.is_secondary_model and not stem_value >= 4: |
|
if self.secondary_model_4_stem[stem_value]: |
|
model_scale = self.secondary_model_4_stem_scale[stem_value] |
|
stem_source_secondary = process_secondary_model(self.secondary_model_4_stem[stem_value], self.process_data, main_model_primary_stem_4_stem=stem_name, is_4_stem_demucs=True) |
|
if isinstance(stem_source_secondary, np.ndarray): |
|
stem_source_secondary = stem_source_secondary[1 if self.secondary_model_4_stem[stem_value].demucs_stem_count == 2 else stem_value] |
|
stem_source_secondary = spec_utils.normalize(stem_source_secondary, self.is_normalization).T |
|
elif type(stem_source_secondary) is dict: |
|
stem_source_secondary = stem_source_secondary[stem_name] |
|
|
|
stem_source_secondary = None if stem_value >= 4 else stem_source_secondary |
|
self.write_to_console(f'{SAVING_STEM[0]}{stem_name}{SAVING_STEM[1]}') if not self.is_secondary_model else None |
|
stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({stem_name}).wav') |
|
stem_source = spec_utils.normalize(source[stem_value], self.is_normalization).T |
|
self.write_audio(stem_path, stem_source, samplerate, secondary_model_source=stem_source_secondary, model_scale=model_scale) |
|
|
|
if self.is_secondary_model: |
|
return source |
|
else: |
|
if self.is_secondary_model_activated: |
|
if self.secondary_model: |
|
self.secondary_source_primary, self.secondary_source_secondary = process_secondary_model(self.secondary_model, self.process_data, main_process_method=self.process_method) |
|
|
|
if not self.is_secondary_stem_only: |
|
self.write_to_console(f'{SAVING_STEM[0]}{self.primary_stem}{SAVING_STEM[1]}') if not self.is_secondary_model else None |
|
primary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({self.primary_stem}).wav') |
|
if not isinstance(self.primary_source, np.ndarray): |
|
self.primary_source = spec_utils.normalize(source[self.demucs_source_map[self.primary_stem]], self.is_normalization).T |
|
self.primary_source_map = {self.primary_stem: self.primary_source} |
|
self.write_audio(primary_stem_path, self.primary_source, samplerate, self.secondary_source_primary) |
|
|
|
if not self.is_primary_stem_only: |
|
def secondary_save(sec_stem_name, source, raw_mixture=None, is_inst_mixture=False): |
|
secondary_source = self.secondary_source if not is_inst_mixture else None |
|
self.write_to_console(f'{SAVING_STEM[0]}{sec_stem_name}{SAVING_STEM[1]}') if not self.is_secondary_model else None |
|
secondary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({sec_stem_name}).wav') |
|
secondary_source_secondary = None |
|
|
|
if not isinstance(secondary_source, np.ndarray): |
|
if self.is_demucs_combine_stems: |
|
source = list(source) |
|
if is_inst_mixture: |
|
source = [i for n, i in enumerate(source) if not n in [self.demucs_source_map[self.primary_stem], self.demucs_source_map[VOCAL_STEM]]] |
|
else: |
|
source.pop(self.demucs_source_map[self.primary_stem]) |
|
|
|
source = source[:len(source) - 2] if is_no_piano_guitar else source |
|
secondary_source = np.zeros_like(source[0]) |
|
for i in source: |
|
secondary_source += i |
|
secondary_source = spec_utils.normalize(secondary_source, self.is_normalization).T |
|
else: |
|
if not isinstance(raw_mixture, np.ndarray): |
|
raw_mixture = prepare_mix(self.audio_file, self.chunks_demucs, self.margin_demucs, is_missing_mix=True) |
|
|
|
secondary_source, raw_mixture = spec_utils.normalize_two_stem(source[self.demucs_source_map[self.primary_stem]], raw_mixture, self.is_normalization) |
|
|
|
if self.is_invert_spec: |
|
secondary_source = spec_utils.invert_stem(raw_mixture, secondary_source) |
|
else: |
|
raw_mixture = spec_utils.reshape_sources(secondary_source, raw_mixture) |
|
secondary_source = (-secondary_source.T+raw_mixture.T) |
|
|
|
if not is_inst_mixture: |
|
self.secondary_source = secondary_source |
|
secondary_source_secondary = self.secondary_source_secondary |
|
self.secondary_source_map = {self.secondary_stem: self.secondary_source} |
|
|
|
self.write_audio(secondary_stem_path, secondary_source, samplerate, secondary_source_secondary) |
|
|
|
secondary_save(self.secondary_stem, source, raw_mixture=raw_mix) |
|
|
|
if self.is_demucs_pre_proc_model_inst_mix and self.pre_proc_model and not self.is_4_stem_ensemble: |
|
secondary_save(f"{self.secondary_stem} {INST_STEM}", source, raw_mixture=inst_raw_mix, is_inst_mixture=True) |
|
|
|
secondary_sources = {**self.primary_source_map, **self.secondary_source_map} |
|
|
|
self.cache_source(secondary_sources) |
|
|
|
if self.is_secondary_model: |
|
return secondary_sources |
|
|
|
def demix_demucs(self, mix): |
|
processed = {} |
|
|
|
set_progress_bar = None if self.is_chunk_demucs else self.set_progress_bar |
|
|
|
for nmix in mix: |
|
self.progress_value += 1 |
|
self.set_progress_bar(0.1, (0.8/len(mix)*self.progress_value)) if self.is_chunk_demucs else None |
|
cmix = mix[nmix] |
|
cmix = torch.tensor(cmix, dtype=torch.float32) |
|
ref = cmix.mean(0) |
|
cmix = (cmix - ref.mean()) / ref.std() |
|
mix_infer = cmix |
|
|
|
with torch.no_grad(): |
|
if self.demucs_version == DEMUCS_V1: |
|
sources = apply_model_v1(self.demucs, |
|
mix_infer.to(self.device), |
|
self.shifts, |
|
self.is_split_mode, |
|
set_progress_bar=set_progress_bar) |
|
elif self.demucs_version == DEMUCS_V2: |
|
sources = apply_model_v2(self.demucs, |
|
mix_infer.to(self.device), |
|
self.shifts, |
|
self.is_split_mode, |
|
self.overlap, |
|
set_progress_bar=set_progress_bar) |
|
else: |
|
sources = apply_model(self.demucs, |
|
mix_infer[None], |
|
self.shifts, |
|
self.is_split_mode, |
|
self.overlap, |
|
static_shifts=1 if self.shifts == 0 else self.shifts, |
|
set_progress_bar=set_progress_bar, |
|
device=self.device)[0] |
|
|
|
sources = (sources * ref.std() + ref.mean()).cpu().numpy() |
|
sources[[0,1]] = sources[[1,0]] |
|
start = 0 if nmix == 0 else self.margin_demucs |
|
end = None if nmix == list(mix.keys())[::-1][0] else -self.margin_demucs |
|
if self.margin_demucs == 0: |
|
end = None |
|
processed[nmix] = sources[:,:,start:end].copy() |
|
sources = list(processed.values()) |
|
sources = np.concatenate(sources, axis=-1) |
|
|
|
return sources |
|
|
|
class SeperateVR(SeperateAttributes): |
|
|
|
def seperate(self): |
|
if self.primary_model_name == self.model_basename and self.primary_sources: |
|
self.primary_source, self.secondary_source = self.load_cached_sources() |
|
else: |
|
self.start_inference_console_write() |
|
if self.is_gpu_conversion >= 0: |
|
if OPERATING_SYSTEM == 'Darwin': |
|
device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu') |
|
else: |
|
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') |
|
else: |
|
device = torch.device('cpu') |
|
|
|
nn_arch_sizes = [ |
|
31191, |
|
33966, 56817, 123821, 123812, 129605, 218409, 537238, 537227] |
|
vr_5_1_models = [56817, 218409] |
|
model_size = math.ceil(os.stat(self.model_path).st_size / 1024) |
|
nn_arch_size = min(nn_arch_sizes, key=lambda x:abs(x-model_size)) |
|
|
|
if nn_arch_size in vr_5_1_models or self.is_vr_51_model: |
|
self.model_run = nets_new.CascadedNet(self.mp.param['bins'] * 2, nn_arch_size, nout=self.model_capacity[0], nout_lstm=self.model_capacity[1]) |
|
else: |
|
self.model_run = nets.determine_model_capacity(self.mp.param['bins'] * 2, nn_arch_size) |
|
|
|
self.model_run.load_state_dict(torch.load(self.model_path, map_location=cpu)) |
|
self.model_run.to(device) |
|
|
|
self.running_inference_console_write() |
|
|
|
y_spec, v_spec = self.inference_vr(self.loading_mix(), device, self.aggressiveness) |
|
self.write_to_console(DONE, base_text='') |
|
|
|
if self.is_secondary_model_activated: |
|
if self.secondary_model: |
|
self.secondary_source_primary, self.secondary_source_secondary = process_secondary_model(self.secondary_model, self.process_data, main_process_method=self.process_method) |
|
|
|
if not self.is_secondary_stem_only: |
|
self.write_to_console(f'{SAVING_STEM[0]}{self.primary_stem}{SAVING_STEM[1]}') if not self.is_secondary_model else None |
|
primary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({self.primary_stem}).wav') |
|
if not isinstance(self.primary_source, np.ndarray): |
|
self.primary_source = spec_utils.normalize(self.spec_to_wav(y_spec), self.is_normalization).T |
|
if not self.model_samplerate == 44100: |
|
self.primary_source = librosa.resample(self.primary_source.T, orig_sr=self.model_samplerate, target_sr=44100).T |
|
|
|
self.primary_source_map = {self.primary_stem: self.primary_source} |
|
|
|
self.write_audio(primary_stem_path, self.primary_source, 44100, self.secondary_source_primary) |
|
|
|
if not self.is_primary_stem_only: |
|
self.write_to_console(f'{SAVING_STEM[0]}{self.secondary_stem}{SAVING_STEM[1]}') if not self.is_secondary_model else None |
|
secondary_stem_path = os.path.join(self.export_path, f'{self.audio_file_base}_({self.secondary_stem}).wav') |
|
if not isinstance(self.secondary_source, np.ndarray): |
|
self.secondary_source = self.spec_to_wav(v_spec) |
|
self.secondary_source = spec_utils.normalize(self.spec_to_wav(v_spec), self.is_normalization).T |
|
if not self.model_samplerate == 44100: |
|
self.secondary_source = librosa.resample(self.secondary_source.T, orig_sr=self.model_samplerate, target_sr=44100).T |
|
|
|
self.secondary_source_map = {self.secondary_stem: self.secondary_source} |
|
|
|
self.write_audio(secondary_stem_path, self.secondary_source, 44100, self.secondary_source_secondary) |
|
|
|
torch.cuda.empty_cache() |
|
secondary_sources = {**self.primary_source_map, **self.secondary_source_map} |
|
self.cache_source(secondary_sources) |
|
|
|
if self.is_secondary_model: |
|
return secondary_sources |
|
|
|
def loading_mix(self): |
|
|
|
X_wave, X_spec_s = {}, {} |
|
|
|
bands_n = len(self.mp.param['band']) |
|
|
|
for d in range(bands_n, 0, -1): |
|
bp = self.mp.param['band'][d] |
|
|
|
if OPERATING_SYSTEM == 'Darwin': |
|
wav_resolution = 'polyphase' if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else bp['res_type'] |
|
else: |
|
wav_resolution = bp['res_type'] |
|
|
|
if d == bands_n: |
|
X_wave[d], _ = librosa.load(self.audio_file, bp['sr'], False, dtype=np.float32, res_type=wav_resolution) |
|
|
|
if not np.any(X_wave[d]) and self.audio_file.endswith('.mp3'): |
|
X_wave[d] = rerun_mp3(self.audio_file, bp['sr']) |
|
|
|
if X_wave[d].ndim == 1: |
|
X_wave[d] = np.asarray([X_wave[d], X_wave[d]]) |
|
else: |
|
X_wave[d] = librosa.resample(X_wave[d+1], self.mp.param['band'][d+1]['sr'], bp['sr'], res_type=wav_resolution) |
|
|
|
X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(X_wave[d], bp['hl'], bp['n_fft'], self.mp.param['mid_side'], |
|
self.mp.param['mid_side_b2'], self.mp.param['reverse']) |
|
|
|
if d == bands_n and self.high_end_process != 'none': |
|
self.input_high_end_h = (bp['n_fft']//2 - bp['crop_stop']) + (self.mp.param['pre_filter_stop'] - self.mp.param['pre_filter_start']) |
|
self.input_high_end = X_spec_s[d][:, bp['n_fft']//2-self.input_high_end_h:bp['n_fft']//2, :] |
|
|
|
X_spec = spec_utils.combine_spectrograms(X_spec_s, self.mp) |
|
|
|
del X_wave, X_spec_s |
|
|
|
return X_spec |
|
|
|
def inference_vr(self, X_spec, device, aggressiveness): |
|
def _execute(X_mag_pad, roi_size): |
|
X_dataset = [] |
|
patches = (X_mag_pad.shape[2] - 2 * self.model_run.offset) // roi_size |
|
total_iterations = patches//self.batch_size if not self.is_tta else (patches//self.batch_size)*2 |
|
for i in range(patches): |
|
start = i * roi_size |
|
X_mag_window = X_mag_pad[:, :, start:start + self.window_size] |
|
X_dataset.append(X_mag_window) |
|
|
|
X_dataset = np.asarray(X_dataset) |
|
self.model_run.eval() |
|
with torch.no_grad(): |
|
mask = [] |
|
for i in range(0, patches, self.batch_size): |
|
self.progress_value += 1 |
|
if self.progress_value >= total_iterations: |
|
self.progress_value = total_iterations |
|
self.set_progress_bar(0.1, 0.8/total_iterations*self.progress_value) |
|
X_batch = X_dataset[i: i + self.batch_size] |
|
X_batch = torch.from_numpy(X_batch).to(device) |
|
pred = self.model_run.predict_mask(X_batch) |
|
if not pred.size()[3] > 0: |
|
raise Exception(ERROR_MAPPER[WINDOW_SIZE_ERROR]) |
|
pred = pred.detach().cpu().numpy() |
|
pred = np.concatenate(pred, axis=2) |
|
mask.append(pred) |
|
if len(mask) == 0: |
|
raise Exception(ERROR_MAPPER[WINDOW_SIZE_ERROR]) |
|
|
|
mask = np.concatenate(mask, axis=2) |
|
return mask |
|
|
|
def postprocess(mask, X_mag, X_phase): |
|
|
|
is_non_accom_stem = False |
|
for stem in NON_ACCOM_STEMS: |
|
if stem == self.primary_stem: |
|
is_non_accom_stem = True |
|
|
|
mask = spec_utils.adjust_aggr(mask, is_non_accom_stem, aggressiveness) |
|
|
|
if self.is_post_process: |
|
mask = spec_utils.merge_artifacts(mask, thres=self.post_process_threshold) |
|
|
|
y_spec = mask * X_mag * np.exp(1.j * X_phase) |
|
v_spec = (1 - mask) * X_mag * np.exp(1.j * X_phase) |
|
|
|
return y_spec, v_spec |
|
X_mag, X_phase = spec_utils.preprocess(X_spec) |
|
n_frame = X_mag.shape[2] |
|
pad_l, pad_r, roi_size = spec_utils.make_padding(n_frame, self.window_size, self.model_run.offset) |
|
X_mag_pad = np.pad(X_mag, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant') |
|
X_mag_pad /= X_mag_pad.max() |
|
mask = _execute(X_mag_pad, roi_size) |
|
|
|
if self.is_tta: |
|
pad_l += roi_size // 2 |
|
pad_r += roi_size // 2 |
|
X_mag_pad = np.pad(X_mag, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant') |
|
X_mag_pad /= X_mag_pad.max() |
|
mask_tta = _execute(X_mag_pad, roi_size) |
|
mask_tta = mask_tta[:, :, roi_size // 2:] |
|
mask = (mask[:, :, :n_frame] + mask_tta[:, :, :n_frame]) * 0.5 |
|
else: |
|
mask = mask[:, :, :n_frame] |
|
|
|
y_spec, v_spec = postprocess(mask, X_mag, X_phase) |
|
|
|
return y_spec, v_spec |
|
|
|
def spec_to_wav(self, spec): |
|
|
|
if self.high_end_process.startswith('mirroring'): |
|
input_high_end_ = spec_utils.mirroring(self.high_end_process, spec, self.input_high_end, self.mp) |
|
wav = spec_utils.cmb_spectrogram_to_wave(spec, self.mp, self.input_high_end_h, input_high_end_) |
|
else: |
|
wav = spec_utils.cmb_spectrogram_to_wave(spec, self.mp) |
|
|
|
return wav |
|
|
|
def process_secondary_model(secondary_model: ModelData, process_data, main_model_primary_stem_4_stem=None, is_4_stem_demucs=False, main_process_method=None, is_pre_proc_model=False): |
|
|
|
if not is_pre_proc_model: |
|
process_iteration = process_data['process_iteration'] |
|
process_iteration() |
|
|
|
if secondary_model.process_method == VR_ARCH_TYPE: |
|
seperator = SeperateVR(secondary_model, process_data, main_model_primary_stem_4_stem=main_model_primary_stem_4_stem, main_process_method=main_process_method) |
|
if secondary_model.process_method == MDX_ARCH_TYPE: |
|
seperator = SeperateMDX(secondary_model, process_data, main_model_primary_stem_4_stem=main_model_primary_stem_4_stem, main_process_method=main_process_method) |
|
if secondary_model.process_method == DEMUCS_ARCH_TYPE: |
|
seperator = SeperateDemucs(secondary_model, process_data, main_model_primary_stem_4_stem=main_model_primary_stem_4_stem, main_process_method=main_process_method) |
|
|
|
secondary_sources = seperator.seperate() |
|
|
|
if type(secondary_sources) is dict and not is_4_stem_demucs and not is_pre_proc_model: |
|
return gather_sources(secondary_model.primary_model_primary_stem, STEM_PAIR_MAPPER[secondary_model.primary_model_primary_stem], secondary_sources) |
|
else: |
|
return secondary_sources |
|
|
|
def gather_sources(primary_stem_name, secondary_stem_name, secondary_sources: dict): |
|
|
|
source_primary = False |
|
source_secondary = False |
|
|
|
for key, value in secondary_sources.items(): |
|
if key in primary_stem_name: |
|
source_primary = value |
|
if key in secondary_stem_name: |
|
source_secondary = value |
|
|
|
return source_primary, source_secondary |
|
|
|
def prepare_mix(mix, chunk_set, margin_set, mdx_net_cut=False, is_missing_mix=False): |
|
|
|
audio_path = mix |
|
samplerate = 44100 |
|
|
|
if not isinstance(mix, np.ndarray): |
|
mix, samplerate = librosa.load(mix, mono=False, sr=44100) |
|
else: |
|
mix = mix.T |
|
|
|
if not np.any(mix) and audio_path.endswith('.mp3'): |
|
mix = rerun_mp3(audio_path) |
|
|
|
if mix.ndim == 1: |
|
mix = np.asfortranarray([mix,mix]) |
|
|
|
def get_segmented_mix(chunk_set=chunk_set): |
|
segmented_mix = {} |
|
|
|
samples = mix.shape[-1] |
|
margin = margin_set |
|
chunk_size = chunk_set*44100 |
|
assert not margin == 0, 'margin cannot be zero!' |
|
|
|
if margin > chunk_size: |
|
margin = chunk_size |
|
if chunk_set == 0 or samples < chunk_size: |
|
chunk_size = samples |
|
|
|
counter = -1 |
|
for skip in range(0, samples, chunk_size): |
|
counter+=1 |
|
s_margin = 0 if counter == 0 else margin |
|
end = min(skip+chunk_size+margin, samples) |
|
start = skip-s_margin |
|
segmented_mix[skip] = mix[:,start:end].copy() |
|
if end == samples: |
|
break |
|
|
|
return segmented_mix |
|
|
|
if is_missing_mix: |
|
return mix |
|
else: |
|
segmented_mix = get_segmented_mix() |
|
raw_mix = get_segmented_mix(chunk_set=0) if mdx_net_cut else mix |
|
return segmented_mix, raw_mix, samplerate |
|
|
|
def rerun_mp3(audio_file, sample_rate=44100): |
|
|
|
with audioread.audio_open(audio_file) as f: |
|
track_length = int(f.duration) |
|
|
|
return librosa.load(audio_file, duration=track_length, mono=False, sr=sample_rate)[0] |
|
|
|
def save_format(audio_path, save_format, mp3_bit_set): |
|
|
|
if not save_format == WAV: |
|
|
|
if OPERATING_SYSTEM == 'Darwin': |
|
FFMPEG_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'ffmpeg') |
|
pydub.AudioSegment.converter = FFMPEG_PATH |
|
|
|
musfile = pydub.AudioSegment.from_wav(audio_path) |
|
|
|
if save_format == FLAC: |
|
audio_path_flac = audio_path.replace(".wav", ".flac") |
|
musfile.export(audio_path_flac, format="flac") |
|
|
|
if save_format == MP3: |
|
audio_path_mp3 = audio_path.replace(".wav", ".mp3") |
|
musfile.export(audio_path_mp3, format="mp3", bitrate=mp3_bit_set) |
|
|
|
try: |
|
os.remove(audio_path) |
|
except Exception as e: |
|
print(e) |
|
|
