# coding: utf-8 __author__ = 'Roman Solovyev (ZFTurbo): https://github.com/ZFTurbo/' import argparse import time import librosa from tqdm.auto import tqdm import sys import os import glob import torch import soundfile as sf import torch.nn as nn import numpy as np from assets.i18n.i18n import I18nAuto # Colab kontrolü try: from google.colab import drive IS_COLAB = True except ImportError: IS_COLAB = False i18n = I18nAuto() current_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.append(current_dir) from utils import demix, get_model_from_config, normalize_audio, denormalize_audio from utils import prefer_target_instrument, apply_tta, load_start_checkpoint, load_lora_weights import warnings warnings.filterwarnings("ignore") def shorten_filename(filename, max_length=30): """Dosya adını belirtilen maksimum uzunluğa kısaltır.""" base, ext = os.path.splitext(filename) if len(base) <= max_length: return filename shortened = base[:15] + "..." + base[-10:] + ext return shortened def get_soundfile_subtype(pcm_type, is_float=False): """PCM türüne göre uygun soundfile alt türünü belirler.""" if is_float: return 'FLOAT' subtype_map = { 'PCM_16': 'PCM_16', 'PCM_24': 'PCM_24', 'FLOAT': 'FLOAT' } return subtype_map.get(pcm_type, 'FLOAT') def run_folder(model, args, config, device, verbose: bool = False): start_time = time.time() model.eval() mixture_paths = sorted(glob.glob(os.path.join(args.input_folder, '*.*'))) sample_rate = getattr(config.audio, 'sample_rate', 44100) print(i18n("total_files_found").format(len(mixture_paths), sample_rate)) instruments = prefer_target_instrument(config)[:] # Çıktı klasörünü kullan (processing.py tarafından ayarlandı) store_dir = args.store_dir os.makedirs(store_dir, exist_ok=True) if not verbose: mixture_paths = tqdm(mixture_paths, desc=i18n("total_progress")) else: mixture_paths = mixture_paths detailed_pbar = not args.disable_detailed_pbar print(i18n("detailed_pbar_enabled").format(detailed_pbar)) for path in mixture_paths: try: mix, sr = librosa.load(path, sr=sample_rate, mono=False) print(i18n("loaded_audio").format(path, mix.shape)) except Exception as e: print(i18n("cannot_read_track").format(path)) print(i18n("error_message").format(str(e))) continue mix_orig = mix.copy() if 'normalize' in config.inference: if config.inference['normalize'] is True: mix, norm_params = normalize_audio(mix) waveforms_orig = demix(config, model, mix, device, model_type=args.model_type, pbar=detailed_pbar) if args.use_tta: waveforms_orig = apply_tta(config, model, mix, waveforms_orig, device, args.model_type) if args.demud_phaseremix_inst: print(i18n("demudding_track").format(path)) instr = 'vocals' if 'vocals' in instruments else instruments[0] instruments.append('instrumental_phaseremix') if 'instrumental' not in instruments and 'Instrumental' not in instruments: mix_modified = mix_orig - 2*waveforms_orig[instr] mix_modified_ = mix_modified.copy() waveforms_modified = demix(config, model, mix_modified, device, model_type=args.model_type, pbar=detailed_pbar) if args.use_tta: waveforms_modified = apply_tta(config, model, mix_modified, waveforms_modified, device, args.model_type) waveforms_orig['instrumental_phaseremix'] = mix_orig + waveforms_modified[instr] else: mix_modified = 2*waveforms_orig[instr] - mix_orig mix_modified_ = mix_modified.copy() waveforms_modified = demix(config, model, mix_modified, device, model_type=args.model_type, pbar=detailed_pbar) if args.use_tta: waveforms_modified = apply_tta(config, model, mix_modified, waveforms_orig, device, args.model_type) waveforms_orig['instrumental_phaseremix'] = mix_orig + mix_modified_ - waveforms_modified[instr] if args.extract_instrumental: instr = 'vocals' if 'vocals' in instruments else instruments[0] waveforms_orig['instrumental'] = mix_orig - waveforms_orig[instr] if 'instrumental' not in instruments: instruments.append('instrumental') for instr in instruments: estimates = waveforms_orig[instr] if 'normalize' in config.inference: if config.inference['normalize'] is True: estimates = denormalize_audio(estimates, norm_params) is_float = getattr(args, 'export_format', '').startswith('wav FLOAT') codec = 'flac' if getattr(args, 'flac_file', False) else 'wav' if codec == 'flac': subtype = get_soundfile_subtype(args.pcm_type, is_float) else: subtype = get_soundfile_subtype('FLOAT', is_float) shortened_filename = shorten_filename(os.path.basename(path)) output_filename = f"{shortened_filename}_{instr}.{codec}" output_path = os.path.join(store_dir, output_filename) sf.write(output_path, estimates.T, sr, subtype=subtype) print(i18n("elapsed_time").format(time.time() - start_time)) def proc_folder(args): parser = argparse.ArgumentParser(description=i18n("proc_folder_description")) parser.add_argument("--model_type", type=str, default='mdx23c', help=i18n("model_type_help")) parser.add_argument("--config_path", type=str, help=i18n("config_path_help")) parser.add_argument("--demud_phaseremix_inst", action='store_true', help=i18n("demud_phaseremix_help")) parser.add_argument("--start_check_point", type=str, default='', help=i18n("start_checkpoint_help")) parser.add_argument("--input_folder", type=str, help=i18n("input_folder_help")) parser.add_argument("--audio_path", type=str, help=i18n("audio_path_help")) parser.add_argument("--store_dir", type=str, default="", help=i18n("store_dir_help")) parser.add_argument("--device_ids", nargs='+', type=int, default=0, help=i18n("device_ids_help")) parser.add_argument("--extract_instrumental", action='store_true', help=i18n("extract_instrumental_help")) parser.add_argument("--disable_detailed_pbar", action='store_true', help=i18n("disable_detailed_pbar_help")) parser.add_argument("--force_cpu", action='store_true', help=i18n("force_cpu_help")) parser.add_argument("--flac_file", action='store_true', help=i18n("flac_file_help")) parser.add_argument("--export_format", type=str, choices=['wav FLOAT', 'flac PCM_16', 'flac PCM_24'], default='flac PCM_24', help=i18n("export_format_help")) parser.add_argument("--pcm_type", type=str, choices=['PCM_16', 'PCM_24'], default='PCM_24', help=i18n("pcm_type_help")) parser.add_argument("--use_tta", action='store_true', help=i18n("use_tta_help")) parser.add_argument("--lora_checkpoint", type=str, default='', help=i18n("lora_checkpoint_help")) parser.add_argument("--chunk_size", type=int, default=1000000, help="Inference chunk size") parser.add_argument("--overlap", type=int, default=4, help="Inference overlap factor") if args is None: args = parser.parse_args() else: args = parser.parse_args(args) device = "cpu" if args.force_cpu: device = "cpu" elif torch.cuda.is_available(): print(i18n("cuda_available")) device = f'cuda:{args.device_ids[0]}' if type(args.device_ids) == list else f'cuda:{args.device_ids}' elif torch.backends.mps.is_available(): device = "mps" print(i18n("using_device").format(device)) model_load_start_time = time.time() torch.backends.cudnn.benchmark = True model, config = get_model_from_config(args.model_type, args.config_path) if args.start_check_point != '': load_start_checkpoint(args, model, type_='inference') print(i18n("instruments_print").format(config.training.instruments)) if type(args.device_ids) == list and len(args.device_ids) > 1 and not args.force_cpu: model = nn.DataParallel(model, device_ids=args.device_ids) model = model.to(device) print(i18n("model_load_time").format(time.time() - model_load_start_time)) run_folder(model, args, config, device, verbose=False) if __name__ == "__main__": proc_folder(None)