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70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
from pathlib import Path
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import os, tempfile
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import numpy as np
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import soundfile as sf
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import librosa
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import torch
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import gc
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from audio_separator.separator import Separator
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def get_vocals(src_path: str, dst_path: str, min_seconds: float = 8) -> str:
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"""
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If the source audio is shorter than `min_seconds`, pad with trailing silence
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in a temporary file, then run separation and save only the vocals to dst_path.
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Returns the full path to the vocals file.
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"""
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default_device = torch.get_default_device()
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torch.set_default_device('cpu')
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dst = Path(dst_path)
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dst.parent.mkdir(parents=True, exist_ok=True)
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# Quick duration check
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duration = librosa.get_duration(path=src_path)
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use_path = src_path
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temp_path = None
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try:
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if duration < min_seconds:
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# Load (resample) and pad in memory
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y, sr = librosa.load(src_path, sr=None, mono=False)
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if y.ndim == 1: # ensure shape (channels, samples)
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y = y[np.newaxis, :]
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target_len = int(min_seconds * sr)
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pad = max(0, target_len - y.shape[1])
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if pad:
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y = np.pad(y, ((0, 0), (0, pad)), mode="constant")
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# Write a temp WAV for the separator
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fd, temp_path = tempfile.mkstemp(suffix=".wav")
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os.close(fd)
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sf.write(temp_path, y.T, sr) # soundfile expects (frames, channels)
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use_path = temp_path
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# Run separation: emit only the vocals, with your exact filename
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sep = Separator(
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output_dir=str(dst.parent),
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output_format=(dst.suffix.lstrip(".") or "wav"),
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output_single_stem="Vocals",
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model_file_dir="ckpts/roformer/" #model_bs_roformer_ep_317_sdr_12.9755.ckpt"
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)
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sep.load_model()
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out_files = sep.separate(use_path, {"Vocals": dst.stem})
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out = Path(out_files[0])
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return str(out if out.is_absolute() else (dst.parent / out))
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finally:
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if temp_path and os.path.exists(temp_path):
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os.remove(temp_path)
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torch.cuda.empty_cache()
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gc.collect()
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torch.set_default_device(default_device)
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# Example:
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# final = extract_vocals("in/clip.mp3", "out/vocals.wav")
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# print(final)
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