import os import sys import yaml import torch import numpy as np from pathlib import Path from hashlib import sha256 sys.path.append(os.getcwd()) from main.configs.config import Config from main.library.uvr5_separator import spec_utils, common_separator from main.library.uvr5_separator.demucs import hdemucs, states, apply translations = Config().translations sys.path.insert(0, os.path.join(os.getcwd(), "main", "library", "uvr5_separator")) DEMUCS_4_SOURCE_MAPPER = {common_separator.CommonSeparator.BASS_STEM: 0, common_separator.CommonSeparator.DRUM_STEM: 1, common_separator.CommonSeparator.OTHER_STEM: 2, common_separator.CommonSeparator.VOCAL_STEM: 3} class DemucsSeparator(common_separator.CommonSeparator): def __init__(self, common_config, arch_config): super().__init__(config=common_config) self.segment_size = arch_config.get("segment_size", "Default") self.shifts = arch_config.get("shifts", 2) self.overlap = arch_config.get("overlap", 0.25) self.segments_enabled = arch_config.get("segments_enabled", True) self.logger.debug(translations["demucs_info"].format(segment_size=self.segment_size, segments_enabled=self.segments_enabled)) self.logger.debug(translations["demucs_info_2"].format(shifts=self.shifts, overlap=self.overlap)) self.demucs_source_map = DEMUCS_4_SOURCE_MAPPER self.audio_file_path = None self.audio_file_base = None self.demucs_model_instance = None self.logger.info(translations["start_demucs"]) def separate(self, audio_file_path): self.logger.debug(translations["start_separator"]) source = None inst_source = {} self.audio_file_path = audio_file_path self.audio_file_base = os.path.splitext(os.path.basename(audio_file_path))[0] self.logger.debug(translations["prepare_mix"]) mix = self.prepare_mix(self.audio_file_path) self.logger.debug(translations["demix"].format(shape=mix.shape)) self.logger.debug(translations["cancel_mix"]) self.demucs_model_instance = hdemucs.HDemucs(sources=["drums", "bass", "other", "vocals"]) self.demucs_model_instance = get_demucs_model(name=os.path.splitext(os.path.basename(self.model_path))[0], repo=Path(os.path.dirname(self.model_path))) self.demucs_model_instance = apply.demucs_segments(self.segment_size, self.demucs_model_instance) self.demucs_model_instance.to(self.torch_device) self.demucs_model_instance.eval() self.logger.debug(translations["model_review"]) source = self.demix_demucs(mix) del self.demucs_model_instance self.clear_gpu_cache() self.logger.debug(translations["del_gpu_cache_after_demix"]) output_files = [] self.logger.debug(translations["process_output_file"]) if isinstance(inst_source, np.ndarray): self.logger.debug(translations["process_ver"]) inst_source[self.demucs_source_map[common_separator.CommonSeparator.VOCAL_STEM]] = spec_utils.reshape_sources(inst_source[self.demucs_source_map[common_separator.CommonSeparator.VOCAL_STEM]], source[self.demucs_source_map[common_separator.CommonSeparator.VOCAL_STEM]]) source = inst_source if isinstance(source, np.ndarray): source_length = len(source) self.logger.debug(translations["source_length"].format(source_length=source_length)) self.logger.debug(translations["set_map"].format(part=source_length)) match source_length: case 2: self.demucs_source_map = {common_separator.CommonSeparator.INST_STEM: 0, common_separator.CommonSeparator.VOCAL_STEM: 1} case 6: self.demucs_source_map = {common_separator.CommonSeparator.BASS_STEM: 0, common_separator.CommonSeparator.DRUM_STEM: 1, common_separator.CommonSeparator.OTHER_STEM: 2, common_separator.CommonSeparator.VOCAL_STEM: 3, common_separator.CommonSeparator.GUITAR_STEM: 4, common_separator.CommonSeparator.PIANO_STEM: 5} case _: self.demucs_source_map = DEMUCS_4_SOURCE_MAPPER self.logger.debug(translations["process_all_part"]) for stem_name, stem_value in self.demucs_source_map.items(): if self.output_single_stem is not None: if stem_name.lower() != self.output_single_stem.lower(): self.logger.debug(translations["skip_part"].format(stem_name=stem_name, output_single_stem=self.output_single_stem)) continue stem_path = os.path.join(f"{self.audio_file_base}_({stem_name})_{self.model_name}.{self.output_format.lower()}") self.final_process(stem_path, source[stem_value].T, stem_name) output_files.append(stem_path) return output_files def demix_demucs(self, mix): self.logger.debug(translations["starting_demix_demucs"]) processed = {} mix = torch.tensor(mix, dtype=torch.float32) ref = mix.mean(0) mix = (mix - ref.mean()) / ref.std() mix_infer = mix with torch.no_grad(): self.logger.debug(translations["model_infer"]) sources = apply.apply_model(model=self.demucs_model_instance, mix=mix_infer[None], shifts=self.shifts, split=self.segments_enabled, overlap=self.overlap, static_shifts=1 if self.shifts == 0 else self.shifts, set_progress_bar=None, device=self.torch_device, progress=True)[0] sources = (sources * ref.std() + ref.mean()).cpu().numpy() sources[[0, 1]] = sources[[1, 0]] processed[mix] = sources[:, :, 0:None].copy() return np.concatenate([s[:, :, 0:None] for s in list(processed.values())], axis=-1) class LocalRepo: def __init__(self, root): self.root = root self.scan() def scan(self): self._models, self._checksums = {}, {} for file in self.root.iterdir(): if file.suffix == ".th": if "-" in file.stem: xp_sig, checksum = file.stem.split("-") self._checksums[xp_sig] = checksum else: xp_sig = file.stem if xp_sig in self._models: raise RuntimeError(translations["del_all_but_one"].format(xp_sig=xp_sig)) self._models[xp_sig] = file def has_model(self, sig): return sig in self._models def get_model(self, sig): try: file = self._models[sig] except KeyError: raise RuntimeError(translations["not_found_model_signature"].format(sig=sig)) if sig in self._checksums: check_checksum(file, self._checksums[sig]) return states.load_model(file) class BagOnlyRepo: def __init__(self, root, model_repo): self.root = root self.model_repo = model_repo self.scan() def scan(self): self._bags = {} for file in self.root.iterdir(): if file.suffix == ".yaml": self._bags[file.stem] = file def get_model(self, name): try: yaml_file = self._bags[name] except KeyError: raise RuntimeError(translations["name_not_pretrained"].format(name=name)) bag = yaml.safe_load(open(yaml_file)) return apply.BagOfModels([self.model_repo.get_model(sig) for sig in bag["models"]], bag.get("weights"), bag.get("segment")) def check_checksum(path, checksum): sha = sha256() with open(path, "rb") as file: while 1: buf = file.read(2**20) if not buf: break sha.update(buf) actual_checksum = sha.hexdigest()[: len(checksum)] if actual_checksum != checksum: raise RuntimeError(translations["invalid_checksum"].format(path=path, checksum=checksum, actual_checksum=actual_checksum)) def get_demucs_model(name, repo = None): model_repo = LocalRepo(repo) return (model_repo.get_model(name) if model_repo.has_model(name) else BagOnlyRepo(repo, model_repo).get_model(name)).eval()