| from pathlib import Path |
| import os, tempfile |
| import numpy as np |
| import soundfile as sf |
| import librosa |
| import torch |
| import gc |
|
|
| from audio_separator.separator import Separator |
|
|
| def _resolve_separator_output(out_file, output_dir: Path) -> Path: |
| out = Path(out_file) |
| return out if out.is_absolute() else output_dir / out |
|
|
|
|
| def _separator_stem_key(path_or_stem) -> str: |
| return "_".join(part for part in Path(path_or_stem).stem.lower().split("_") if part) |
|
|
|
|
| def _prepare_separator_input(src_path: str, min_seconds: float): |
| duration = librosa.get_duration(path=src_path) |
| use_path = src_path |
| temp_path = None |
|
|
| if duration < min_seconds: |
| |
| y, sr = librosa.load(src_path, sr=None, mono=False) |
| if y.ndim == 1: |
| y = y[np.newaxis, :] |
| target_len = int(min_seconds * sr) |
| pad = max(0, target_len - y.shape[1]) |
| if pad: |
| y = np.pad(y, ((0, 0), (0, pad)), mode="constant") |
|
|
| |
| fd, temp_path = tempfile.mkstemp(suffix=".wav") |
| os.close(fd) |
| sf.write(temp_path, y.T, sr) |
| use_path = temp_path |
|
|
| return use_path, temp_path |
|
|
|
|
| def extract_vocal_and_background_stems(src_path: str, vocals_dst_path: str, background_dst_path: str, min_seconds: float = 8) -> tuple[str, str]: |
| default_device = torch.get_default_device() |
| torch.set_default_device('cpu') |
|
|
| vocals_dst = Path(vocals_dst_path) |
| background_dst = Path(background_dst_path) |
| vocals_dst.parent.mkdir(parents=True, exist_ok=True) |
| background_dst.parent.mkdir(parents=True, exist_ok=True) |
|
|
| sep = None |
| temp_path = None |
| try: |
| use_path, temp_path = _prepare_separator_input(src_path, min_seconds) |
| sep = Separator( |
| output_dir=str(vocals_dst.parent), |
| output_format=(vocals_dst.suffix.lstrip(".") or "wav"), |
| model_file_dir="ckpts/roformer/" |
| ) |
| sep.load_model() |
| out_files = sep.separate(use_path, {"Vocals": vocals_dst.stem, "Instrumental": background_dst.stem}) |
| out_paths = [_resolve_separator_output(out_file, vocals_dst.parent) for out_file in out_files] |
| by_stem = {_separator_stem_key(out_path): str(out_path) for out_path in out_paths} |
| return by_stem[_separator_stem_key(vocals_dst)], by_stem[_separator_stem_key(background_dst)] |
| finally: |
| if sep is not None: |
| del sep |
| if temp_path and os.path.exists(temp_path): |
| os.remove(temp_path) |
|
|
| torch.cuda.empty_cache() |
| gc.collect() |
| torch.set_default_device(default_device) |
|
|
|
|
| def get_vocals(src_path: str, dst_path: str, min_seconds: float = 8) -> str: |
| """ |
| If the source audio is shorter than `min_seconds`, pad with trailing silence |
| in a temporary file, then run separation and save only the vocals to dst_path. |
| Returns the full path to the vocals file. |
| """ |
|
|
| default_device = torch.get_default_device() |
| torch.set_default_device('cpu') |
|
|
| dst = Path(dst_path) |
| dst.parent.mkdir(parents=True, exist_ok=True) |
|
|
| sep = None |
| temp_path = None |
| try: |
| use_path, temp_path = _prepare_separator_input(src_path, min_seconds) |
| |
| sep = Separator( |
| output_dir=str(dst.parent), |
| output_format=(dst.suffix.lstrip(".") or "wav"), |
| output_single_stem="Vocals", |
| model_file_dir="ckpts/roformer/" |
| ) |
| sep.load_model() |
| out_files = sep.separate(use_path, {"Vocals": dst.stem}) |
|
|
| return str(_resolve_separator_output(out_files[0], dst.parent)) |
| finally: |
| if sep is not None: |
| del sep |
| if temp_path and os.path.exists(temp_path): |
| os.remove(temp_path) |
|
|
| torch.cuda.empty_cache() |
| gc.collect() |
| torch.set_default_device(default_device) |
|
|
| |
| |
| |
|
|
|
|
