| import os | |
| import sys | |
| import yaml | |
| import torch | |
| import warnings | |
| import numpy as np | |
| from hashlib import sha256 | |
| sys.path.append(os.getcwd()) | |
| from main.app.variables import config | |
| from main.library.uvr5_lib import spec_utils, common_separator | |
| from main.library.uvr5_lib.demucs import hdemucs, states, apply | |
| warnings.filterwarnings("ignore") | |
| sys.path.insert(0, os.path.join(os.getcwd(), "main", "library", "uvr5_lib")) | |
| 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.demucs_source_map = DEMUCS_4_SOURCE_MAPPER | |
| self.audio_file_path = None | |
| self.audio_file_base = None | |
| self.demucs_model_instance = None | |
| if config.configs.get("demucs_cpu_mode", False): self.torch_device = torch.device("cpu") | |
| def separate(self, audio_file_path): | |
| 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] | |
| mix = self.prepare_mix(self.audio_file_path) | |
| 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=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() | |
| source = self.demix_demucs(mix) | |
| del self.demucs_model_instance | |
| self.clear_gpu_cache() | |
| output_files = [] | |
| if isinstance(inst_source, np.ndarray): | |
| 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) | |
| if source_length == 2: self.demucs_source_map = {common_separator.CommonSeparator.INST_STEM: 0, common_separator.CommonSeparator.VOCAL_STEM: 1} | |
| elif source_length == 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} | |
| else: self.demucs_source_map = DEMUCS_4_SOURCE_MAPPER | |
| 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(): | |
| 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): | |
| 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(): | |
| 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 filename in os.listdir(self.root): | |
| filepath = os.path.join(self.root, filename) | |
| if not os.path.isfile(filepath): continue | |
| if os.path.splitext(filename)[1] == ".th": | |
| stem = os.path.splitext(filename)[0] | |
| if "-" in stem: | |
| xp_sig, checksum = stem.split("-", 1) | |
| self._checksums[xp_sig] = checksum | |
| else: xp_sig = stem | |
| if xp_sig in self._models: raise RuntimeError | |
| self._models[xp_sig] = filepath | |
| def has_model(self, sig): | |
| return sig in self._models | |
| def get_model(self, sig): | |
| try: | |
| file = self._models[sig] | |
| except KeyError: | |
| raise RuntimeError | |
| 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 filename in os.listdir(self.root): | |
| filepath = os.path.join(self.root, filename) | |
| if os.path.isfile(filepath) and os.path.splitext(filename)[1] == ".yaml": | |
| stem = os.path.splitext(filename)[0] | |
| self._bags[stem] = filepath | |
| def get_model(self, name): | |
| try: | |
| yaml_file = self._bags[name] | |
| except KeyError: | |
| raise RuntimeError | |
| with open(yaml_file, 'r') as f: | |
| bag = yaml.safe_load(f) | |
| 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 | |
| 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() |