Spaces:
Runtime error
Runtime error
File size: 4,323 Bytes
7bc29af |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import json
from fractions import Fraction
from concurrent import futures
import musdb
from torch import distributed
from .audio import AudioFile
def get_musdb_tracks(root, *args, **kwargs):
mus = musdb.DB(root, *args, **kwargs)
return {track.name: track.path for track in mus}
class StemsSet:
def __init__(self, tracks, metadata, duration=None, stride=1,
samplerate=44100, channels=2, streams=slice(None)):
self.metadata = []
for name, path in tracks.items():
meta = dict(metadata[name])
meta["path"] = path
meta["name"] = name
self.metadata.append(meta)
if duration is not None and meta["duration"] < duration:
raise ValueError(f"Track {name} duration is too small {meta['duration']}")
self.metadata.sort(key=lambda x: x["name"])
self.duration = duration
self.stride = stride
self.channels = channels
self.samplerate = samplerate
self.streams = streams
def __len__(self):
return sum(self._examples_count(m) for m in self.metadata)
def _examples_count(self, meta):
if self.duration is None:
return 1
else:
return int((meta["duration"] - self.duration) // self.stride + 1)
def track_metadata(self, index):
for meta in self.metadata:
examples = self._examples_count(meta)
if index >= examples:
index -= examples
continue
return meta
def __getitem__(self, index):
for meta in self.metadata:
examples = self._examples_count(meta)
if index >= examples:
index -= examples
continue
streams = AudioFile(meta["path"]).read(seek_time=index * self.stride,
duration=self.duration,
channels=self.channels,
samplerate=self.samplerate,
streams=self.streams)
return (streams - meta["mean"]) / meta["std"]
def _get_track_metadata(path):
# use mono at 44kHz as reference. For any other settings data won't be perfectly
# normalized but it should be good enough.
audio = AudioFile(path)
mix = audio.read(streams=0, channels=1, samplerate=44100)
return {"duration": audio.duration, "std": mix.std().item(), "mean": mix.mean().item()}
def _build_metadata(tracks, workers=10):
pendings = []
with futures.ProcessPoolExecutor(workers) as pool:
for name, path in tracks.items():
pendings.append((name, pool.submit(_get_track_metadata, path)))
return {name: p.result() for name, p in pendings}
def _build_musdb_metadata(path, musdb, workers):
tracks = get_musdb_tracks(musdb)
metadata = _build_metadata(tracks, workers)
path.parent.mkdir(exist_ok=True, parents=True)
json.dump(metadata, open(path, "w"))
def get_compressed_datasets(args, samples):
metadata_file = args.metadata / "musdb.json"
if not metadata_file.is_file() and args.rank == 0:
_build_musdb_metadata(metadata_file, args.musdb, args.workers)
if args.world_size > 1:
distributed.barrier()
metadata = json.load(open(metadata_file))
duration = Fraction(samples, args.samplerate)
stride = Fraction(args.data_stride, args.samplerate)
train_set = StemsSet(get_musdb_tracks(args.musdb, subsets=["train"], split="train"),
metadata,
duration=duration,
stride=stride,
streams=slice(1, None),
samplerate=args.samplerate,
channels=args.audio_channels)
valid_set = StemsSet(get_musdb_tracks(args.musdb, subsets=["train"], split="valid"),
metadata,
samplerate=args.samplerate,
channels=args.audio_channels)
return train_set, valid_set
|