mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-28 09:21:03 +01:00
116 lines
4.2 KiB
Python
116 lines
4.2 KiB
Python
# 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 json
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from fractions import Fraction
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from concurrent import futures
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import musdb
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from torch import distributed
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from .audio import AudioFile
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def get_musdb_tracks(root, *args, **kwargs):
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mus = musdb.DB(root, *args, **kwargs)
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return {track.name: track.path for track in mus}
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class StemsSet:
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def __init__(self, tracks, metadata, duration=None, stride=1,
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samplerate=44100, channels=2, streams=slice(None)):
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self.metadata = []
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for name, path in tracks.items():
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meta = dict(metadata[name])
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meta["path"] = path
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meta["name"] = name
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self.metadata.append(meta)
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if duration is not None and meta["duration"] < duration:
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raise ValueError(f"Track {name} duration is too small {meta['duration']}")
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self.metadata.sort(key=lambda x: x["name"])
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self.duration = duration
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self.stride = stride
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self.channels = channels
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self.samplerate = samplerate
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self.streams = streams
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def __len__(self):
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return sum(self._examples_count(m) for m in self.metadata)
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def _examples_count(self, meta):
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if self.duration is None:
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return 1
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else:
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return int((meta["duration"] - self.duration) // self.stride + 1)
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def track_metadata(self, index):
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for meta in self.metadata:
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examples = self._examples_count(meta)
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if index >= examples:
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index -= examples
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continue
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return meta
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def __getitem__(self, index):
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for meta in self.metadata:
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examples = self._examples_count(meta)
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if index >= examples:
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index -= examples
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continue
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streams = AudioFile(meta["path"]).read(seek_time=index * self.stride,
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duration=self.duration,
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channels=self.channels,
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samplerate=self.samplerate,
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streams=self.streams)
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return (streams - meta["mean"]) / meta["std"]
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def _get_track_metadata(path):
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# use mono at 44kHz as reference. For any other settings data won't be perfectly
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# normalized but it should be good enough.
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audio = AudioFile(path)
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mix = audio.read(streams=0, channels=1, samplerate=44100)
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return {"duration": audio.duration, "std": mix.std().item(), "mean": mix.mean().item()}
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def _build_metadata(tracks, workers=10):
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pendings = []
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with futures.ProcessPoolExecutor(workers) as pool:
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for name, path in tracks.items():
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pendings.append((name, pool.submit(_get_track_metadata, path)))
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return {name: p.result() for name, p in pendings}
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def _build_musdb_metadata(path, musdb, workers):
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tracks = get_musdb_tracks(musdb)
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metadata = _build_metadata(tracks, workers)
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path.parent.mkdir(exist_ok=True, parents=True)
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json.dump(metadata, open(path, "w"))
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def get_compressed_datasets(args, samples):
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metadata_file = args.metadata / "musdb.json"
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if not metadata_file.is_file() and args.rank == 0:
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_build_musdb_metadata(metadata_file, args.musdb, args.workers)
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if args.world_size > 1:
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distributed.barrier()
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metadata = json.load(open(metadata_file))
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duration = Fraction(samples, args.samplerate)
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stride = Fraction(args.data_stride, args.samplerate)
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train_set = StemsSet(get_musdb_tracks(args.musdb, subsets=["train"], split="train"),
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metadata,
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duration=duration,
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stride=stride,
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streams=slice(1, None),
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samplerate=args.samplerate,
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channels=args.audio_channels)
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valid_set = StemsSet(get_musdb_tracks(args.musdb, subsets=["train"], split="valid"),
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metadata,
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samplerate=args.samplerate,
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channels=args.audio_channels)
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return train_set, valid_set
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