Delete demucs directory
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Anjok07
GitHub
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# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import argparse
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import sys
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from pathlib import Path
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import subprocess
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import julius
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import torch as th
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import torchaudio as ta
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from .audio import AudioFile, convert_audio_channels
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from .pretrained import is_pretrained, load_pretrained
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from .utils import apply_model, load_model
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def load_track(track, device, audio_channels, samplerate):
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errors = {}
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wav = None
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try:
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wav = AudioFile(track).read(
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streams=0,
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samplerate=samplerate,
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channels=audio_channels).to(device)
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except FileNotFoundError:
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errors['ffmpeg'] = 'Ffmpeg is not installed.'
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except subprocess.CalledProcessError:
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errors['ffmpeg'] = 'FFmpeg could not read the file.'
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if wav is None:
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try:
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wav, sr = ta.load(str(track))
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except RuntimeError as err:
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errors['torchaudio'] = err.args[0]
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else:
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wav = convert_audio_channels(wav, audio_channels)
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wav = wav.to(device)
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wav = julius.resample_frac(wav, sr, samplerate)
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if wav is None:
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print(f"Could not load file {track}. "
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"Maybe it is not a supported file format? ")
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for backend, error in errors.items():
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print(f"When trying to load using {backend}, got the following error: {error}")
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sys.exit(1)
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return wav
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def encode_mp3(wav, path, bitrate=320, samplerate=44100, channels=2, verbose=False):
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try:
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import lameenc
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except ImportError:
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print("Failed to call lame encoder. Maybe it is not installed? "
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"On windows, run `python.exe -m pip install -U lameenc`, "
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"on OSX/Linux, run `python3 -m pip install -U lameenc`, "
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"then try again.", file=sys.stderr)
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sys.exit(1)
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encoder = lameenc.Encoder()
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encoder.set_bit_rate(bitrate)
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encoder.set_in_sample_rate(samplerate)
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encoder.set_channels(channels)
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encoder.set_quality(2) # 2-highest, 7-fastest
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if not verbose:
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encoder.silence()
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wav = wav.transpose(0, 1).numpy()
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mp3_data = encoder.encode(wav.tobytes())
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mp3_data += encoder.flush()
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with open(path, "wb") as f:
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f.write(mp3_data)
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def main():
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parser = argparse.ArgumentParser("demucs.separate",
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description="Separate the sources for the given tracks")
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parser.add_argument("tracks", nargs='+', type=Path, default=[], help='Path to tracks')
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parser.add_argument("-n",
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"--name",
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default="demucs_quantized",
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help="Model name. See README.md for the list of pretrained models. "
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"Default is demucs_quantized.")
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parser.add_argument("-v", "--verbose", action="store_true")
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parser.add_argument("-o",
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"--out",
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type=Path,
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default=Path("separated"),
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help="Folder where to put extracted tracks. A subfolder "
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"with the model name will be created.")
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parser.add_argument("--models",
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type=Path,
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default=Path("models"),
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help="Path to trained models. "
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"Also used to store downloaded pretrained models")
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parser.add_argument("-d",
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"--device",
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default="cuda" if th.cuda.is_available() else "cpu",
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help="Device to use, default is cuda if available else cpu")
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parser.add_argument("--shifts",
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default=0,
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type=int,
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help="Number of random shifts for equivariant stabilization."
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"Increase separation time but improves quality for Demucs. 10 was used "
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"in the original paper.")
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parser.add_argument("--overlap",
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default=0.25,
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type=float,
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help="Overlap between the splits.")
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parser.add_argument("--no-split",
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action="store_false",
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dest="split",
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default=True,
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help="Doesn't split audio in chunks. This can use large amounts of memory.")
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parser.add_argument("--float32",
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action="store_true",
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help="Convert the output wavefile to use pcm f32 format instead of s16. "
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"This should not make a difference if you just plan on listening to the "
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"audio but might be needed to compute exactly metrics like SDR etc.")
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parser.add_argument("--int16",
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action="store_false",
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dest="float32",
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help="Opposite of --float32, here for compatibility.")
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parser.add_argument("--mp3", action="store_true",
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help="Convert the output wavs to mp3.")
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parser.add_argument("--mp3-bitrate",
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default=320,
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type=int,
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help="Bitrate of converted mp3.")
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args = parser.parse_args()
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name = args.name + ".th"
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model_path = args.models / name
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if model_path.is_file():
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model = load_model(model_path)
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else:
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if is_pretrained(args.name):
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model = load_pretrained(args.name)
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else:
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print(f"No pre-trained model {args.name}", file=sys.stderr)
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sys.exit(1)
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model.to(args.device)
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out = args.out / args.name
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out.mkdir(parents=True, exist_ok=True)
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print(f"Separated tracks will be stored in {out.resolve()}")
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for track in args.tracks:
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if not track.exists():
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print(
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f"File {track} does not exist. If the path contains spaces, "
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"please try again after surrounding the entire path with quotes \"\".",
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file=sys.stderr)
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continue
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print(f"Separating track {track}")
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wav = load_track(track, args.device, model.audio_channels, model.samplerate)
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ref = wav.mean(0)
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wav = (wav - ref.mean()) / ref.std()
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sources = apply_model(model, wav, shifts=args.shifts, split=args.split,
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overlap=args.overlap, progress=True)
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sources = sources * ref.std() + ref.mean()
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track_folder = out / track.name.rsplit(".", 1)[0]
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track_folder.mkdir(exist_ok=True)
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for source, name in zip(sources, model.sources):
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source = source / max(1.01 * source.abs().max(), 1)
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if args.mp3 or not args.float32:
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source = (source * 2**15).clamp_(-2**15, 2**15 - 1).short()
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source = source.cpu()
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stem = str(track_folder / name)
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if args.mp3:
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encode_mp3(source, stem + ".mp3",
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bitrate=args.mp3_bitrate,
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samplerate=model.samplerate,
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channels=model.audio_channels,
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verbose=args.verbose)
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else:
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wavname = str(track_folder / f"{name}.wav")
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ta.save(wavname, source, sample_rate=model.samplerate)
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if __name__ == "__main__":
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main()
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