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| import audioread | |
| import librosa | |
| import os | |
| import sys | |
| import json | |
| import time | |
| from tqdm import tqdm | |
| import pickle | |
| import hashlib | |
| import logging | |
| import traceback | |
| import shutil | |
| import soundfile as sf | |
| import torch | |
| from gui_data.constants import * | |
| from gui_data.old_data_check import file_check, remove_unneeded_yamls, remove_temps | |
| from lib_v5.vr_network.model_param_init import ModelParameters | |
| from lib_v5 import spec_utils | |
| from pathlib import Path | |
| from separate import SeperateAttributes, SeperateDemucs, SeperateMDX, SeperateVR, save_format | |
| from typing import List | |
| logging.basicConfig(format='%(asctime)s - %(message)s', level=logging.INFO) | |
| logging.info('UVR BEGIN') | |
| PREVIOUS_PATCH_WIN = 'UVR_Patch_1_12_23_14_54' | |
| is_dnd_compatible = True | |
| banner_placement = -2 | |
| def save_data(data): | |
| """ | |
| Saves given data as a .pkl (pickle) file | |
| Paramters: | |
| data(dict): | |
| Dictionary containing all the necessary data to save | |
| """ | |
| # Open data file, create it if it does not exist | |
| with open('data.pkl', 'wb') as data_file: | |
| pickle.dump(data, data_file) | |
| def load_data() -> dict: | |
| """ | |
| Loads saved pkl file and returns the stored data | |
| Returns(dict): | |
| Dictionary containing all the saved data | |
| """ | |
| try: | |
| with open('data.pkl', 'rb') as data_file: # Open data file | |
| data = pickle.load(data_file) | |
| return data | |
| except (ValueError, FileNotFoundError): | |
| # Data File is corrupted or not found so recreate it | |
| save_data(data=DEFAULT_DATA) | |
| return load_data() | |
| def load_model_hash_data(dictionary): | |
| '''Get the model hash dictionary''' | |
| with open(dictionary) as d: | |
| data = d.read() | |
| return json.loads(data) | |
| # Change the current working directory to the directory | |
| # this file sits in | |
| if getattr(sys, 'frozen', False): | |
| # If the application is run as a bundle, the PyInstaller bootloader | |
| # extends the sys module by a flag frozen=True and sets the app | |
| # path into variable _MEIPASS'. | |
| BASE_PATH = sys._MEIPASS | |
| else: | |
| BASE_PATH = os.path.dirname(os.path.abspath(__file__)) | |
| os.chdir(BASE_PATH) # Change the current working directory to the base path | |
| debugger = [] | |
| #--Constants-- | |
| #Models | |
| MODELS_DIR = os.path.join(BASE_PATH, 'models') | |
| VR_MODELS_DIR = os.path.join(MODELS_DIR, 'VR_Models') | |
| MDX_MODELS_DIR = os.path.join(MODELS_DIR, 'MDX_Net_Models') | |
| DEMUCS_MODELS_DIR = os.path.join(MODELS_DIR, 'Demucs_Models') | |
| DEMUCS_NEWER_REPO_DIR = os.path.join(DEMUCS_MODELS_DIR, 'v3_v4_repo') | |
| MDX_MIXER_PATH = os.path.join(BASE_PATH, 'lib_v5', 'mixer.ckpt') | |
| #Cache & Parameters | |
| VR_HASH_DIR = os.path.join(VR_MODELS_DIR, 'model_data') | |
| VR_HASH_JSON = os.path.join(VR_MODELS_DIR, 'model_data', 'model_data.json') | |
| MDX_HASH_DIR = os.path.join(MDX_MODELS_DIR, 'model_data') | |
| MDX_HASH_JSON = os.path.join(MDX_MODELS_DIR, 'model_data', 'model_data.json') | |
| DEMUCS_MODEL_NAME_SELECT = os.path.join(DEMUCS_MODELS_DIR, 'model_data', 'model_name_mapper.json') | |
| MDX_MODEL_NAME_SELECT = os.path.join(MDX_MODELS_DIR, 'model_data', 'model_name_mapper.json') | |
| ENSEMBLE_CACHE_DIR = os.path.join(BASE_PATH, 'gui_data', 'saved_ensembles') | |
| SETTINGS_CACHE_DIR = os.path.join(BASE_PATH, 'gui_data', 'saved_settings') | |
| VR_PARAM_DIR = os.path.join(BASE_PATH, 'lib_v5', 'vr_network', 'modelparams') | |
| SAMPLE_CLIP_PATH = os.path.join(BASE_PATH, 'temp_sample_clips') | |
| ENSEMBLE_TEMP_PATH = os.path.join(BASE_PATH, 'ensemble_temps') | |
| #Style | |
| ICON_IMG_PATH = os.path.join(BASE_PATH, 'gui_data', 'img', 'GUI-Icon.ico') | |
| FONT_PATH = os.path.join(BASE_PATH, 'gui_data', 'fonts', 'centurygothic', 'GOTHIC.TTF')#ensemble_temps | |
| #Other | |
| COMPLETE_CHIME = os.path.join(BASE_PATH, 'gui_data', 'complete_chime.wav') | |
| FAIL_CHIME = os.path.join(BASE_PATH, 'gui_data', 'fail_chime.wav') | |
| CHANGE_LOG = os.path.join(BASE_PATH, 'gui_data', 'change_log.txt') | |
| SPLASH_DOC = os.path.join(BASE_PATH, 'tmp', 'splash.txt') | |
| file_check(os.path.join(MODELS_DIR, 'Main_Models'), VR_MODELS_DIR) | |
| file_check(os.path.join(DEMUCS_MODELS_DIR, 'v3_repo'), DEMUCS_NEWER_REPO_DIR) | |
| remove_unneeded_yamls(DEMUCS_MODELS_DIR) | |
| remove_temps(ENSEMBLE_TEMP_PATH) | |
| remove_temps(SAMPLE_CLIP_PATH) | |
| remove_temps(os.path.join(BASE_PATH, 'img')) | |
| if not os.path.isdir(ENSEMBLE_TEMP_PATH): | |
| os.mkdir(ENSEMBLE_TEMP_PATH) | |
| if not os.path.isdir(SAMPLE_CLIP_PATH): | |
| os.mkdir(SAMPLE_CLIP_PATH) | |
| model_hash_table = {} | |
| data = load_data() | |
| class ModelData(): | |
| def __init__(self, model_name: str, | |
| selected_process_method=ENSEMBLE_MODE, | |
| is_secondary_model=False, | |
| primary_model_primary_stem=None, | |
| is_primary_model_primary_stem_only=False, | |
| is_primary_model_secondary_stem_only=False, | |
| is_pre_proc_model=False, | |
| is_dry_check=False): | |
| self.is_gpu_conversion = 0 if root.is_gpu_conversion_var.get() else -1 | |
| self.is_normalization = root.is_normalization_var.get() | |
| self.is_primary_stem_only = root.is_primary_stem_only_var.get() | |
| self.is_secondary_stem_only = root.is_secondary_stem_only_var.get() | |
| self.is_denoise = root.is_denoise_var.get() | |
| self.mdx_batch_size = 1 if root.mdx_batch_size_var.get() == DEF_OPT else int(root.mdx_batch_size_var.get()) | |
| self.is_mdx_ckpt = False | |
| self.wav_type_set = root.wav_type_set | |
| self.mp3_bit_set = root.mp3_bit_set_var.get() | |
| self.save_format = root.save_format_var.get() | |
| self.is_invert_spec = root.is_invert_spec_var.get() | |
| self.is_mixer_mode = root.is_mixer_mode_var.get() | |
| self.demucs_stems = root.demucs_stems_var.get() | |
| self.demucs_source_list = [] | |
| self.demucs_stem_count = 0 | |
| self.mixer_path = MDX_MIXER_PATH | |
| self.model_name = model_name | |
| self.process_method = selected_process_method | |
| self.model_status = False if self.model_name == CHOOSE_MODEL or self.model_name == NO_MODEL else True | |
| self.primary_stem = None | |
| self.secondary_stem = None | |
| self.is_ensemble_mode = False | |
| self.ensemble_primary_stem = None | |
| self.ensemble_secondary_stem = None | |
| self.primary_model_primary_stem = primary_model_primary_stem | |
| self.is_secondary_model = is_secondary_model | |
| self.secondary_model = None | |
| self.secondary_model_scale = None | |
| self.demucs_4_stem_added_count = 0 | |
| self.is_demucs_4_stem_secondaries = False | |
| self.is_4_stem_ensemble = False | |
| self.pre_proc_model = None | |
| self.pre_proc_model_activated = False | |
| self.is_pre_proc_model = is_pre_proc_model | |
| self.is_dry_check = is_dry_check | |
| self.model_samplerate = 44100 | |
| self.model_capacity = 32, 128 | |
| self.is_vr_51_model = False | |
| self.is_demucs_pre_proc_model_inst_mix = False | |
| self.manual_download_Button = None | |
| self.secondary_model_4_stem = [] | |
| self.secondary_model_4_stem_scale = [] | |
| self.secondary_model_4_stem_names = [] | |
| self.secondary_model_4_stem_model_names_list = [] | |
| self.all_models = [] | |
| self.secondary_model_other = None | |
| self.secondary_model_scale_other = None | |
| self.secondary_model_bass = None | |
| self.secondary_model_scale_bass = None | |
| self.secondary_model_drums = None | |
| self.secondary_model_scale_drums = None | |
| if selected_process_method == ENSEMBLE_MODE: | |
| partitioned_name = model_name.partition(ENSEMBLE_PARTITION) | |
| self.process_method = partitioned_name[0] | |
| self.model_name = partitioned_name[2] | |
| self.model_and_process_tag = model_name | |
| self.ensemble_primary_stem, self.ensemble_secondary_stem = root.return_ensemble_stems() | |
| self.is_ensemble_mode = True if not is_secondary_model and not is_pre_proc_model else False | |
| self.is_4_stem_ensemble = True if root.ensemble_main_stem_var.get() == FOUR_STEM_ENSEMBLE and self.is_ensemble_mode else False | |
| self.pre_proc_model_activated = root.is_demucs_pre_proc_model_activate_var.get() if not self.ensemble_primary_stem == VOCAL_STEM else False | |
| if self.process_method == VR_ARCH_TYPE: | |
| self.is_secondary_model_activated = root.vr_is_secondary_model_activate_var.get() if not self.is_secondary_model else False | |
| self.aggression_setting = float(int(root.aggression_setting_var.get())/100) | |
| self.is_tta = root.is_tta_var.get() | |
| self.is_post_process = root.is_post_process_var.get() | |
| self.window_size = int(root.window_size_var.get()) | |
| self.batch_size = 1 if root.batch_size_var.get() == DEF_OPT else int(root.batch_size_var.get()) | |
| self.crop_size = int(root.crop_size_var.get()) | |
| self.is_high_end_process = 'mirroring' if root.is_high_end_process_var.get() else 'None' | |
| self.post_process_threshold = float(root.post_process_threshold_var.get()) | |
| self.model_capacity = 32, 128 | |
| self.model_path = os.path.join(VR_MODELS_DIR, f"{self.model_name}.pth") | |
| self.get_model_hash() | |
| if self.model_hash: | |
| self.model_data = self.get_model_data(VR_HASH_DIR, root.vr_hash_MAPPER) if not self.model_hash == WOOD_INST_MODEL_HASH else WOOD_INST_PARAMS | |
| if self.model_data: | |
| vr_model_param = os.path.join(VR_PARAM_DIR, "{}.json".format(self.model_data["vr_model_param"])) | |
| self.primary_stem = self.model_data["primary_stem"] | |
| self.secondary_stem = STEM_PAIR_MAPPER[self.primary_stem] | |
| self.vr_model_param = ModelParameters(vr_model_param) | |
| self.model_samplerate = self.vr_model_param.param['sr'] | |
| if "nout" in self.model_data.keys() and "nout_lstm" in self.model_data.keys(): | |
| self.model_capacity = self.model_data["nout"], self.model_data["nout_lstm"] | |
| self.is_vr_51_model = True | |
| else: | |
| self.model_status = False | |
| if self.process_method == MDX_ARCH_TYPE: | |
| self.is_secondary_model_activated = root.mdx_is_secondary_model_activate_var.get() if not is_secondary_model else False | |
| self.margin = int(root.margin_var.get()) | |
| self.chunks = root.determine_auto_chunks(root.chunks_var.get(), self.is_gpu_conversion) if root.is_chunk_mdxnet_var.get() else 0 | |
| self.get_mdx_model_path() | |
| self.get_model_hash() | |
| if self.model_hash: | |
| self.model_data = self.get_model_data(MDX_HASH_DIR, root.mdx_hash_MAPPER) | |
| if self.model_data: | |
| self.compensate = self.model_data["compensate"] if root.compensate_var.get() == AUTO_SELECT else float(root.compensate_var.get()) | |
| self.mdx_dim_f_set = self.model_data["mdx_dim_f_set"] | |
| self.mdx_dim_t_set = self.model_data["mdx_dim_t_set"] | |
| self.mdx_n_fft_scale_set = self.model_data["mdx_n_fft_scale_set"] | |
| self.primary_stem = self.model_data["primary_stem"] | |
| self.secondary_stem = STEM_PAIR_MAPPER[self.primary_stem] | |
| else: | |
| self.model_status = False | |
| if self.process_method == DEMUCS_ARCH_TYPE: | |
| self.is_secondary_model_activated = root.demucs_is_secondary_model_activate_var.get() if not is_secondary_model else False | |
| if not self.is_ensemble_mode: | |
| self.pre_proc_model_activated = root.is_demucs_pre_proc_model_activate_var.get() if not root.demucs_stems_var.get() in [VOCAL_STEM, INST_STEM] else False | |
| self.overlap = float(root.overlap_var.get()) | |
| self.margin_demucs = int(root.margin_demucs_var.get()) | |
| self.chunks_demucs = root.determine_auto_chunks(root.chunks_demucs_var.get(), self.is_gpu_conversion) | |
| self.shifts = int(root.shifts_var.get()) | |
| self.is_split_mode = root.is_split_mode_var.get() | |
| self.segment = root.segment_var.get() | |
| self.is_chunk_demucs = root.is_chunk_demucs_var.get() | |
| self.is_demucs_combine_stems = root.is_demucs_combine_stems_var.get() | |
| self.is_primary_stem_only = root.is_primary_stem_only_var.get() if self.is_ensemble_mode else root.is_primary_stem_only_Demucs_var.get() | |
| self.is_secondary_stem_only = root.is_secondary_stem_only_var.get() if self.is_ensemble_mode else root.is_secondary_stem_only_Demucs_var.get() | |
| self.get_demucs_model_path() | |
| self.get_demucs_model_data() | |
| self.model_basename = os.path.splitext(os.path.basename(self.model_path))[0] if self.model_status else None | |
| self.pre_proc_model_activated = self.pre_proc_model_activated if not self.is_secondary_model else False | |
| self.is_primary_model_primary_stem_only = is_primary_model_primary_stem_only | |
| self.is_primary_model_secondary_stem_only = is_primary_model_secondary_stem_only | |
| if self.is_secondary_model_activated and self.model_status: | |
| if (not self.is_ensemble_mode and root.demucs_stems_var.get() == ALL_STEMS and self.process_method == DEMUCS_ARCH_TYPE) or self.is_4_stem_ensemble: | |
| for key in DEMUCS_4_SOURCE_LIST: | |
| self.secondary_model_data(key) | |
| self.secondary_model_4_stem.append(self.secondary_model) | |
| self.secondary_model_4_stem_scale.append(self.secondary_model_scale) | |
| self.secondary_model_4_stem_names.append(key) | |
| self.demucs_4_stem_added_count = sum(i is not None for i in self.secondary_model_4_stem) | |
| self.is_secondary_model_activated = False if all(i is None for i in self.secondary_model_4_stem) else True | |
| self.demucs_4_stem_added_count = self.demucs_4_stem_added_count - 1 if self.is_secondary_model_activated else self.demucs_4_stem_added_count | |
| if self.is_secondary_model_activated: | |
| self.secondary_model_4_stem_model_names_list = [None if i is None else i.model_basename for i in self.secondary_model_4_stem] | |
| self.is_demucs_4_stem_secondaries = True | |
| else: | |
| primary_stem = self.ensemble_primary_stem if self.is_ensemble_mode and self.process_method == DEMUCS_ARCH_TYPE else self.primary_stem | |
| self.secondary_model_data(primary_stem) | |
| if self.process_method == DEMUCS_ARCH_TYPE and not is_secondary_model: | |
| if self.demucs_stem_count >= 3 and self.pre_proc_model_activated: | |
| self.pre_proc_model_activated = True | |
| self.pre_proc_model = root.process_determine_demucs_pre_proc_model(self.primary_stem) | |
| self.is_demucs_pre_proc_model_inst_mix = root.is_demucs_pre_proc_model_inst_mix_var.get() if self.pre_proc_model else False | |
| def secondary_model_data(self, primary_stem): | |
| secondary_model_data = root.process_determine_secondary_model(self.process_method, primary_stem, self.is_primary_stem_only, self.is_secondary_stem_only) | |
| self.secondary_model = secondary_model_data[0] | |
| self.secondary_model_scale = secondary_model_data[1] | |
| self.is_secondary_model_activated = False if not self.secondary_model else True | |
| if self.secondary_model: | |
| self.is_secondary_model_activated = False if self.secondary_model.model_basename == self.model_basename else True | |
| def get_mdx_model_path(self): | |
| if self.model_name.endswith(CKPT): | |
| # self.chunks = 0 | |
| # self.is_mdx_batch_mode = True | |
| self.is_mdx_ckpt = True | |
| ext = '' if self.is_mdx_ckpt else ONNX | |
| for file_name, chosen_mdx_model in root.mdx_name_select_MAPPER.items(): | |
| if self.model_name in chosen_mdx_model: | |
| self.model_path = os.path.join(MDX_MODELS_DIR, f"{file_name}{ext}") | |
| break | |
| else: | |
| self.model_path = os.path.join(MDX_MODELS_DIR, f"{self.model_name}{ext}") | |
| self.mixer_path = os.path.join(MDX_MODELS_DIR, f"mixer_val.ckpt") | |
| def get_demucs_model_path(self): | |
| demucs_newer = [True for x in DEMUCS_NEWER_TAGS if x in self.model_name] | |
| demucs_model_dir = DEMUCS_NEWER_REPO_DIR if demucs_newer else DEMUCS_MODELS_DIR | |
| for file_name, chosen_model in root.demucs_name_select_MAPPER.items(): | |
| if self.model_name in chosen_model: | |
| self.model_path = os.path.join(demucs_model_dir, file_name) | |
| break | |
| else: | |
| self.model_path = os.path.join(DEMUCS_NEWER_REPO_DIR, f'{self.model_name}.yaml') | |
| def get_demucs_model_data(self): | |
| self.demucs_version = DEMUCS_V4 | |
| for key, value in DEMUCS_VERSION_MAPPER.items(): | |
| if value in self.model_name: | |
| self.demucs_version = key | |
| self.demucs_source_list = DEMUCS_2_SOURCE if DEMUCS_UVR_MODEL in self.model_name else DEMUCS_4_SOURCE | |
| self.demucs_source_map = DEMUCS_2_SOURCE_MAPPER if DEMUCS_UVR_MODEL in self.model_name else DEMUCS_4_SOURCE_MAPPER | |
| self.demucs_stem_count = 2 if DEMUCS_UVR_MODEL in self.model_name else 4 | |
| if not self.is_ensemble_mode: | |
| self.primary_stem = PRIMARY_STEM if self.demucs_stems == ALL_STEMS else self.demucs_stems | |
| self.secondary_stem = STEM_PAIR_MAPPER[self.primary_stem] | |
| def get_model_data(self, model_hash_dir, hash_mapper): | |
| model_settings_json = os.path.join(model_hash_dir, "{}.json".format(self.model_hash)) | |
| if os.path.isfile(model_settings_json): | |
| return json.load(open(model_settings_json)) | |
| else: | |
| for hash, settings in hash_mapper.items(): | |
| if self.model_hash in hash: | |
| return settings | |
| else: | |
| return self.get_model_data_from_popup() | |
| def get_model_data_from_popup(self): | |
| return None | |
| def get_model_hash(self): | |
| self.model_hash = None | |
| if not os.path.isfile(self.model_path): | |
| self.model_status = False | |
| self.model_hash is None | |
| else: | |
| if model_hash_table: | |
| for (key, value) in model_hash_table.items(): | |
| if self.model_path == key: | |
| self.model_hash = value | |
| break | |
| if not self.model_hash: | |
| try: | |
| with open(self.model_path, 'rb') as f: | |
| f.seek(- 10000 * 1024, 2) | |
| self.model_hash = hashlib.md5(f.read()).hexdigest() | |
| except: | |
| self.model_hash = hashlib.md5(open(self.model_path,'rb').read()).hexdigest() | |
| table_entry = {self.model_path: self.model_hash} | |
| model_hash_table.update(table_entry) | |
| class Ensembler(): | |
| def __init__(self, is_manual_ensemble=False): | |
| self.is_save_all_outputs_ensemble = root.is_save_all_outputs_ensemble_var.get() | |
| chosen_ensemble_name = '{}'.format(root.chosen_ensemble_var.get().replace(" ", "_")) if not root.chosen_ensemble_var.get() == CHOOSE_ENSEMBLE_OPTION else 'Ensembled' | |
| ensemble_algorithm = root.ensemble_type_var.get().partition("/") | |
| ensemble_main_stem_pair = root.ensemble_main_stem_var.get().partition("/") | |
| time_stamp = round(time.time()) | |
| self.audio_tool = MANUAL_ENSEMBLE | |
| self.main_export_path = Path(root.export_path_var.get()) | |
| self.chosen_ensemble = f"_{chosen_ensemble_name}" if root.is_append_ensemble_name_var.get() else '' | |
| ensemble_folder_name = self.main_export_path if self.is_save_all_outputs_ensemble else ENSEMBLE_TEMP_PATH | |
| self.ensemble_folder_name = os.path.join(ensemble_folder_name, '{}_Outputs_{}'.format(chosen_ensemble_name, time_stamp)) | |
| self.is_testing_audio = f"{time_stamp}_" if root.is_testing_audio_var.get() else '' | |
| self.primary_algorithm = ensemble_algorithm[0] | |
| self.secondary_algorithm = ensemble_algorithm[2] | |
| self.ensemble_primary_stem = ensemble_main_stem_pair[0] | |
| self.ensemble_secondary_stem = ensemble_main_stem_pair[2] | |
| self.is_normalization = root.is_normalization_var.get() | |
| self.wav_type_set = root.wav_type_set | |
| self.mp3_bit_set = root.mp3_bit_set_var.get() | |
| self.save_format = root.save_format_var.get() | |
| if not is_manual_ensemble: | |
| os.mkdir(self.ensemble_folder_name) | |
| def ensemble_outputs(self, audio_file_base, export_path, stem, is_4_stem=False, is_inst_mix=False): | |
| """Processes the given outputs and ensembles them with the chosen algorithm""" | |
| if is_4_stem: | |
| algorithm = root.ensemble_type_var.get() | |
| stem_tag = stem | |
| else: | |
| if is_inst_mix: | |
| algorithm = self.secondary_algorithm | |
| stem_tag = f"{self.ensemble_secondary_stem} {INST_STEM}" | |
| else: | |
| algorithm = self.primary_algorithm if stem == PRIMARY_STEM else self.secondary_algorithm | |
| stem_tag = self.ensemble_primary_stem if stem == PRIMARY_STEM else self.ensemble_secondary_stem | |
| stem_outputs = self.get_files_to_ensemble(folder=export_path, prefix=audio_file_base, suffix=f"_({stem_tag}).wav") | |
| audio_file_output = f"{self.is_testing_audio}{audio_file_base}{self.chosen_ensemble}_({stem_tag})" | |
| stem_save_path = os.path.join('{}'.format(self.main_export_path),'{}.wav'.format(audio_file_output)) | |
| if stem_outputs: | |
| spec_utils.ensemble_inputs(stem_outputs, algorithm, self.is_normalization, self.wav_type_set, stem_save_path) | |
| save_format(stem_save_path, self.save_format, self.mp3_bit_set) | |
| if self.is_save_all_outputs_ensemble: | |
| for i in stem_outputs: | |
| save_format(i, self.save_format, self.mp3_bit_set) | |
| else: | |
| for i in stem_outputs: | |
| try: | |
| os.remove(i) | |
| except Exception as e: | |
| print(e) | |
| def ensemble_manual(self, audio_inputs, audio_file_base, is_bulk=False): | |
| """Processes the given outputs and ensembles them with the chosen algorithm""" | |
| is_mv_sep = True | |
| if is_bulk: | |
| number_list = list(set([os.path.basename(i).split("_")[0] for i in audio_inputs])) | |
| for n in number_list: | |
| current_list = [i for i in audio_inputs if os.path.basename(i).startswith(n)] | |
| audio_file_base = os.path.basename(current_list[0]).split('.wav')[0] | |
| stem_testing = "instrum" if "Instrumental" in audio_file_base else "vocals" | |
| if is_mv_sep: | |
| audio_file_base = audio_file_base.split("_") | |
| audio_file_base = f"{audio_file_base[1]}_{audio_file_base[2]}_{stem_testing}" | |
| self.ensemble_manual_process(current_list, audio_file_base, is_bulk) | |
| else: | |
| self.ensemble_manual_process(audio_inputs, audio_file_base, is_bulk) | |
| def ensemble_manual_process(self, audio_inputs, audio_file_base, is_bulk): | |
| algorithm = root.choose_algorithm_var.get() | |
| algorithm_text = "" if is_bulk else f"_({root.choose_algorithm_var.get()})" | |
| stem_save_path = os.path.join('{}'.format(self.main_export_path),'{}{}{}.wav'.format(self.is_testing_audio, audio_file_base, algorithm_text)) | |
| spec_utils.ensemble_inputs(audio_inputs, algorithm, self.is_normalization, self.wav_type_set, stem_save_path) | |
| save_format(stem_save_path, self.save_format, self.mp3_bit_set) | |
| def get_files_to_ensemble(self, folder="", prefix="", suffix=""): | |
| """Grab all the files to be ensembled""" | |
| return [os.path.join(folder, i) for i in os.listdir(folder) if i.startswith(prefix) and i.endswith(suffix)] | |
| def secondary_stem(stem): | |
| """Determines secondary stem""" | |
| for key, value in STEM_PAIR_MAPPER.items(): | |
| if stem in key: | |
| secondary_stem = value | |
| return secondary_stem | |
| class UVRInterface: | |
| def __init__(self) -> None: | |
| pass | |
| def assemble_model_data(self, model=None, arch_type=ENSEMBLE_MODE, is_dry_check=False) -> List[ModelData]: | |
| if arch_type == ENSEMBLE_STEM_CHECK: | |
| model_data = self.model_data_table | |
| missing_models = [model.model_status for model in model_data if not model.model_status] | |
| if missing_models or not model_data: | |
| model_data: List[ModelData] = [ModelData(model_name, is_dry_check=is_dry_check) for model_name in self.ensemble_model_list] | |
| self.model_data_table = model_data | |
| if arch_type == ENSEMBLE_MODE: | |
| model_data: List[ModelData] = [ModelData(model_name) for model_name in self.ensemble_listbox_get_all_selected_models()] | |
| if arch_type == ENSEMBLE_CHECK: | |
| model_data: List[ModelData] = [ModelData(model)] | |
| if arch_type == VR_ARCH_TYPE or arch_type == VR_ARCH_PM: | |
| model_data: List[ModelData] = [ModelData(model, VR_ARCH_TYPE)] | |
| if arch_type == MDX_ARCH_TYPE: | |
| model_data: List[ModelData] = [ModelData(model, MDX_ARCH_TYPE)] | |
| if arch_type == DEMUCS_ARCH_TYPE: | |
| model_data: List[ModelData] = [ModelData(model, DEMUCS_ARCH_TYPE)]# | |
| return model_data | |
| def create_sample(self, audio_file, sample_path=SAMPLE_CLIP_PATH): | |
| try: | |
| with audioread.audio_open(audio_file) as f: | |
| track_length = int(f.duration) | |
| except Exception as e: | |
| print('Audioread failed to get duration. Trying Librosa...') | |
| y, sr = librosa.load(audio_file, mono=False, sr=44100) | |
| track_length = int(librosa.get_duration(y=y, sr=sr)) | |
| clip_duration = int(root.model_sample_mode_duration_var.get()) | |
| if track_length >= clip_duration: | |
| offset_cut = track_length//3 | |
| off_cut = offset_cut + track_length | |
| if not off_cut >= clip_duration: | |
| offset_cut = 0 | |
| name_apped = f'{clip_duration}_second_' | |
| else: | |
| offset_cut, clip_duration = 0, track_length | |
| name_apped = '' | |
| sample = librosa.load(audio_file, offset=offset_cut, duration=clip_duration, mono=False, sr=44100)[0].T | |
| audio_sample = os.path.join(sample_path, f'{os.path.splitext(os.path.basename(audio_file))[0]}_{name_apped}sample.wav') | |
| sf.write(audio_sample, sample, 44100) | |
| return audio_sample | |
| def verify_audio(self, audio_file, is_process=True, sample_path=None): | |
| is_good = False | |
| error_data = '' | |
| if os.path.isfile(audio_file): | |
| try: | |
| librosa.load(audio_file, duration=3, mono=False, sr=44100) if not type(sample_path) is str else self.create_sample(audio_file, sample_path) | |
| is_good = True | |
| except Exception as e: | |
| error_name = f'{type(e).__name__}' | |
| traceback_text = ''.join(traceback.format_tb(e.__traceback__)) | |
| message = f'{error_name}: "{e}"\n{traceback_text}"' | |
| if is_process: | |
| audio_base_name = os.path.basename(audio_file) | |
| self.error_log_var.set(f'Error Loading the Following File:\n\n\"{audio_base_name}\"\n\nRaw Error Details:\n\n{message}') | |
| else: | |
| error_data = AUDIO_VERIFICATION_CHECK(audio_file, message) | |
| if is_process: | |
| return is_good | |
| else: | |
| return is_good, error_data | |
| def cached_sources_clear(self): | |
| self.vr_cache_source_mapper = {} | |
| self.mdx_cache_source_mapper = {} | |
| self.demucs_cache_source_mapper = {} | |
| def cached_model_source_holder(self, process_method, sources, model_name=None): | |
| if process_method == VR_ARCH_TYPE: | |
| self.vr_cache_source_mapper = {**self.vr_cache_source_mapper, **{model_name: sources}} | |
| if process_method == MDX_ARCH_TYPE: | |
| self.mdx_cache_source_mapper = {**self.mdx_cache_source_mapper, **{model_name: sources}} | |
| if process_method == DEMUCS_ARCH_TYPE: | |
| self.demucs_cache_source_mapper = {**self.demucs_cache_source_mapper, **{model_name: sources}} | |
| def cached_source_callback(self, process_method, model_name=None): | |
| model, sources = None, None | |
| if process_method == VR_ARCH_TYPE: | |
| mapper = self.vr_cache_source_mapper | |
| if process_method == MDX_ARCH_TYPE: | |
| mapper = self.mdx_cache_source_mapper | |
| if process_method == DEMUCS_ARCH_TYPE: | |
| mapper = self.demucs_cache_source_mapper | |
| for key, value in mapper.items(): | |
| if model_name in key: | |
| model = key | |
| sources = value | |
| return model, sources | |
| def cached_source_model_list_check(self, model_list: List[ModelData]): | |
| model: ModelData | |
| primary_model_names = lambda process_method:[model.model_basename if model.process_method == process_method else None for model in model_list] | |
| secondary_model_names = lambda process_method:[model.secondary_model.model_basename if model.is_secondary_model_activated and model.process_method == process_method else None for model in model_list] | |
| self.vr_primary_model_names = primary_model_names(VR_ARCH_TYPE) | |
| self.mdx_primary_model_names = primary_model_names(MDX_ARCH_TYPE) | |
| self.demucs_primary_model_names = primary_model_names(DEMUCS_ARCH_TYPE) | |
| self.vr_secondary_model_names = secondary_model_names(VR_ARCH_TYPE) | |
| self.mdx_secondary_model_names = secondary_model_names(MDX_ARCH_TYPE) | |
| self.demucs_secondary_model_names = [model.secondary_model.model_basename if model.is_secondary_model_activated and model.process_method == DEMUCS_ARCH_TYPE and not model.secondary_model is None else None for model in model_list] | |
| self.demucs_pre_proc_model_name = [model.pre_proc_model.model_basename if model.pre_proc_model else None for model in model_list]#list(dict.fromkeys()) | |
| for model in model_list: | |
| if model.process_method == DEMUCS_ARCH_TYPE and model.is_demucs_4_stem_secondaries: | |
| if not model.is_4_stem_ensemble: | |
| self.demucs_secondary_model_names = model.secondary_model_4_stem_model_names_list | |
| break | |
| else: | |
| for i in model.secondary_model_4_stem_model_names_list: | |
| self.demucs_secondary_model_names.append(i) | |
| self.all_models = self.vr_primary_model_names + self.mdx_primary_model_names + self.demucs_primary_model_names + self.vr_secondary_model_names + self.mdx_secondary_model_names + self.demucs_secondary_model_names + self.demucs_pre_proc_model_name | |
| def process(self, model_name, arch_type, audio_file, export_path, is_model_sample_mode=False, is_4_stem_ensemble=False, set_progress_func=None, console_write=print) -> SeperateAttributes: | |
| stime = time.perf_counter() | |
| time_elapsed = lambda:f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}' | |
| if arch_type==ENSEMBLE_MODE: | |
| model_list, ensemble = self.assemble_model_data(), Ensembler() | |
| export_path = ensemble.ensemble_folder_name | |
| is_ensemble = True | |
| else: | |
| model_list = self.assemble_model_data(model_name, arch_type) | |
| is_ensemble = False | |
| self.cached_source_model_list_check(model_list) | |
| model = model_list[0] | |
| if self.verify_audio(audio_file): | |
| audio_file = self.create_sample(audio_file) if is_model_sample_mode else audio_file | |
| else: | |
| print(f'"{os.path.basename(audio_file)}\" is missing or currupted.\n') | |
| exit() | |
| audio_file_base = f"{os.path.splitext(os.path.basename(audio_file))[0]}" | |
| audio_file_base = audio_file_base if is_ensemble else f"{round(time.time())}_{audio_file_base}" | |
| audio_file_base = audio_file_base if not is_ensemble else f"{audio_file_base}_{model.model_basename}" | |
| if not is_ensemble: | |
| audio_file_base = f"{audio_file_base}_{model.model_basename}" | |
| if not is_ensemble: | |
| export_path = os.path.join(Path(export_path), model.model_basename, os.path.splitext(os.path.basename(audio_file))[0]) | |
| if not os.path.isdir(export_path): | |
| os.makedirs(export_path) | |
| if set_progress_func is None: | |
| pbar = tqdm(total=1) | |
| self._progress = 0 | |
| def set_progress_func(step, inference_iterations=0): | |
| progress_curr = step + inference_iterations | |
| pbar.update(progress_curr-self._progress) | |
| self._progress = progress_curr | |
| def postprocess(): | |
| pbar.close() | |
| else: | |
| def postprocess(): | |
| pass | |
| process_data = { | |
| 'model_data': model, | |
| 'export_path': export_path, | |
| 'audio_file_base': audio_file_base, | |
| 'audio_file': audio_file, | |
| 'set_progress_bar': set_progress_func, | |
| 'write_to_console': lambda progress_text, base_text='': console_write(base_text + progress_text), | |
| 'process_iteration': lambda:None, | |
| 'cached_source_callback': self.cached_source_callback, | |
| 'cached_model_source_holder': self.cached_model_source_holder, | |
| 'list_all_models': self.all_models, | |
| 'is_ensemble_master': is_ensemble, | |
| 'is_4_stem_ensemble': is_ensemble and is_4_stem_ensemble | |
| } | |
| if model.process_method == VR_ARCH_TYPE: | |
| seperator = SeperateVR(model, process_data) | |
| if model.process_method == MDX_ARCH_TYPE: | |
| seperator = SeperateMDX(model, process_data) | |
| if model.process_method == DEMUCS_ARCH_TYPE: | |
| seperator = SeperateDemucs(model, process_data) | |
| seperator.seperate() | |
| postprocess() | |
| if is_ensemble: | |
| audio_file_base = audio_file_base.replace(f"_{model.model_basename}", "") | |
| console_write(ENSEMBLING_OUTPUTS) | |
| if is_4_stem_ensemble: | |
| for output_stem in DEMUCS_4_SOURCE_LIST: | |
| ensemble.ensemble_outputs(audio_file_base, export_path, output_stem, is_4_stem=True) | |
| else: | |
| if not root.is_secondary_stem_only_var.get(): | |
| ensemble.ensemble_outputs(audio_file_base, export_path, PRIMARY_STEM) | |
| if not root.is_primary_stem_only_var.get(): | |
| ensemble.ensemble_outputs(audio_file_base, export_path, SECONDARY_STEM) | |
| ensemble.ensemble_outputs(audio_file_base, export_path, SECONDARY_STEM, is_inst_mix=True) | |
| console_write(DONE) | |
| if is_model_sample_mode: | |
| if os.path.isfile(audio_file): | |
| os.remove(audio_file) | |
| torch.cuda.empty_cache() | |
| if is_ensemble and len(os.listdir(export_path)) == 0: | |
| shutil.rmtree(export_path) | |
| console_write(f'Process Complete, using time: {time_elapsed()}\nOutput path: {export_path}') | |
| self.cached_sources_clear() | |
| return seperator | |
| class RootWrapper: | |
| def __init__(self, var) -> None: | |
| self.var=var | |
| def set(self, val): | |
| self.var=val | |
| def get(self): | |
| return self.var | |
| class FakeRoot: | |
| def __init__(self) -> None: | |
| self.wav_type_set = 'PCM_16' | |
| self.vr_hash_MAPPER = load_model_hash_data(VR_HASH_JSON) | |
| self.mdx_hash_MAPPER = load_model_hash_data(MDX_HASH_JSON) | |
| self.mdx_name_select_MAPPER = load_model_hash_data(MDX_MODEL_NAME_SELECT) | |
| self.demucs_name_select_MAPPER = load_model_hash_data(DEMUCS_MODEL_NAME_SELECT) | |
| def __getattribute__(self, __name: str): | |
| try: | |
| return super().__getattribute__(__name) | |
| except AttributeError: | |
| wrapped=RootWrapper(None) | |
| super().__setattr__(__name, wrapped) | |
| return wrapped | |
| def load_saved_settings(self, loaded_setting: dict, process_method=None): | |
| """Loads user saved application settings or resets to default""" | |
| for key, value in DEFAULT_DATA.items(): | |
| if not key in loaded_setting.keys(): | |
| loaded_setting = {**loaded_setting, **{key:value}} | |
| loaded_setting['batch_size'] = DEF_OPT | |
| is_ensemble = True if process_method == ENSEMBLE_MODE else False | |
| if not process_method or process_method == VR_ARCH_PM or is_ensemble: | |
| self.vr_model_var.set(loaded_setting['vr_model']) | |
| self.aggression_setting_var.set(loaded_setting['aggression_setting']) | |
| self.window_size_var.set(loaded_setting['window_size']) | |
| self.batch_size_var.set(loaded_setting['batch_size']) | |
| self.crop_size_var.set(loaded_setting['crop_size']) | |
| self.is_tta_var.set(loaded_setting['is_tta']) | |
| self.is_output_image_var.set(loaded_setting['is_output_image']) | |
| self.is_post_process_var.set(loaded_setting['is_post_process']) | |
| self.is_high_end_process_var.set(loaded_setting['is_high_end_process']) | |
| self.post_process_threshold_var.set(loaded_setting['post_process_threshold']) | |
| self.vr_voc_inst_secondary_model_var.set(loaded_setting['vr_voc_inst_secondary_model']) | |
| self.vr_other_secondary_model_var.set(loaded_setting['vr_other_secondary_model']) | |
| self.vr_bass_secondary_model_var.set(loaded_setting['vr_bass_secondary_model']) | |
| self.vr_drums_secondary_model_var.set(loaded_setting['vr_drums_secondary_model']) | |
| self.vr_is_secondary_model_activate_var.set(loaded_setting['vr_is_secondary_model_activate']) | |
| self.vr_voc_inst_secondary_model_scale_var.set(loaded_setting['vr_voc_inst_secondary_model_scale']) | |
| self.vr_other_secondary_model_scale_var.set(loaded_setting['vr_other_secondary_model_scale']) | |
| self.vr_bass_secondary_model_scale_var.set(loaded_setting['vr_bass_secondary_model_scale']) | |
| self.vr_drums_secondary_model_scale_var.set(loaded_setting['vr_drums_secondary_model_scale']) | |
| if not process_method or process_method == DEMUCS_ARCH_TYPE or is_ensemble: | |
| self.demucs_model_var.set(loaded_setting['demucs_model']) | |
| self.segment_var.set(loaded_setting['segment']) | |
| self.overlap_var.set(loaded_setting['overlap']) | |
| self.shifts_var.set(loaded_setting['shifts']) | |
| self.chunks_demucs_var.set(loaded_setting['chunks_demucs']) | |
| self.margin_demucs_var.set(loaded_setting['margin_demucs']) | |
| self.is_chunk_demucs_var.set(loaded_setting['is_chunk_demucs']) | |
| self.is_chunk_mdxnet_var.set(loaded_setting['is_chunk_mdxnet']) | |
| self.is_primary_stem_only_Demucs_var.set(loaded_setting['is_primary_stem_only_Demucs']) | |
| self.is_secondary_stem_only_Demucs_var.set(loaded_setting['is_secondary_stem_only_Demucs']) | |
| self.is_split_mode_var.set(loaded_setting['is_split_mode']) | |
| self.is_demucs_combine_stems_var.set(loaded_setting['is_demucs_combine_stems']) | |
| self.demucs_voc_inst_secondary_model_var.set(loaded_setting['demucs_voc_inst_secondary_model']) | |
| self.demucs_other_secondary_model_var.set(loaded_setting['demucs_other_secondary_model']) | |
| self.demucs_bass_secondary_model_var.set(loaded_setting['demucs_bass_secondary_model']) | |
| self.demucs_drums_secondary_model_var.set(loaded_setting['demucs_drums_secondary_model']) | |
| self.demucs_is_secondary_model_activate_var.set(loaded_setting['demucs_is_secondary_model_activate']) | |
| self.demucs_voc_inst_secondary_model_scale_var.set(loaded_setting['demucs_voc_inst_secondary_model_scale']) | |
| self.demucs_other_secondary_model_scale_var.set(loaded_setting['demucs_other_secondary_model_scale']) | |
| self.demucs_bass_secondary_model_scale_var.set(loaded_setting['demucs_bass_secondary_model_scale']) | |
| self.demucs_drums_secondary_model_scale_var.set(loaded_setting['demucs_drums_secondary_model_scale']) | |
| self.demucs_stems_var.set(loaded_setting['demucs_stems']) | |
| # self.update_stem_checkbox_labels(self.demucs_stems_var.get(), demucs=True) | |
| self.demucs_pre_proc_model_var.set(data['demucs_pre_proc_model']) | |
| self.is_demucs_pre_proc_model_activate_var.set(data['is_demucs_pre_proc_model_activate']) | |
| self.is_demucs_pre_proc_model_inst_mix_var.set(data['is_demucs_pre_proc_model_inst_mix']) | |
| if not process_method or process_method == MDX_ARCH_TYPE or is_ensemble: | |
| self.mdx_net_model_var.set(loaded_setting['mdx_net_model']) | |
| self.chunks_var.set(loaded_setting['chunks']) | |
| self.margin_var.set(loaded_setting['margin']) | |
| self.compensate_var.set(loaded_setting['compensate']) | |
| self.is_denoise_var.set(loaded_setting['is_denoise']) | |
| self.is_invert_spec_var.set(loaded_setting['is_invert_spec']) | |
| self.is_mixer_mode_var.set(loaded_setting['is_mixer_mode']) | |
| self.mdx_batch_size_var.set(loaded_setting['mdx_batch_size']) | |
| self.mdx_voc_inst_secondary_model_var.set(loaded_setting['mdx_voc_inst_secondary_model']) | |
| self.mdx_other_secondary_model_var.set(loaded_setting['mdx_other_secondary_model']) | |
| self.mdx_bass_secondary_model_var.set(loaded_setting['mdx_bass_secondary_model']) | |
| self.mdx_drums_secondary_model_var.set(loaded_setting['mdx_drums_secondary_model']) | |
| self.mdx_is_secondary_model_activate_var.set(loaded_setting['mdx_is_secondary_model_activate']) | |
| self.mdx_voc_inst_secondary_model_scale_var.set(loaded_setting['mdx_voc_inst_secondary_model_scale']) | |
| self.mdx_other_secondary_model_scale_var.set(loaded_setting['mdx_other_secondary_model_scale']) | |
| self.mdx_bass_secondary_model_scale_var.set(loaded_setting['mdx_bass_secondary_model_scale']) | |
| self.mdx_drums_secondary_model_scale_var.set(loaded_setting['mdx_drums_secondary_model_scale']) | |
| if not process_method or is_ensemble: | |
| self.is_save_all_outputs_ensemble_var.set(loaded_setting['is_save_all_outputs_ensemble']) | |
| self.is_append_ensemble_name_var.set(loaded_setting['is_append_ensemble_name']) | |
| self.chosen_audio_tool_var.set(loaded_setting['chosen_audio_tool']) | |
| self.choose_algorithm_var.set(loaded_setting['choose_algorithm']) | |
| self.time_stretch_rate_var.set(loaded_setting['time_stretch_rate']) | |
| self.pitch_rate_var.set(loaded_setting['pitch_rate']) | |
| self.is_primary_stem_only_var.set(loaded_setting['is_primary_stem_only']) | |
| self.is_secondary_stem_only_var.set(loaded_setting['is_secondary_stem_only']) | |
| self.is_testing_audio_var.set(loaded_setting['is_testing_audio']) | |
| self.is_add_model_name_var.set(loaded_setting['is_add_model_name']) | |
| self.is_accept_any_input_var.set(loaded_setting["is_accept_any_input"]) | |
| self.is_task_complete_var.set(loaded_setting['is_task_complete']) | |
| self.is_create_model_folder_var.set(loaded_setting['is_create_model_folder']) | |
| self.mp3_bit_set_var.set(loaded_setting['mp3_bit_set']) | |
| self.save_format_var.set(loaded_setting['save_format']) | |
| self.wav_type_set_var.set(loaded_setting['wav_type_set']) | |
| self.user_code_var.set(loaded_setting['user_code']) | |
| self.is_gpu_conversion_var.set(loaded_setting['is_gpu_conversion']) | |
| self.is_normalization_var.set(loaded_setting['is_normalization']) | |
| self.help_hints_var.set(loaded_setting['help_hints_var']) | |
| self.model_sample_mode_var.set(loaded_setting['model_sample_mode']) | |
| self.model_sample_mode_duration_var.set(loaded_setting['model_sample_mode_duration']) | |
| root = FakeRoot() | |
| root.load_saved_settings(DEFAULT_DATA) |