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# 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.
from collections import OrderedDict
import hashlib
import math
import json
from pathlib import Path
import julius
import torch as th
from torch import distributed
import torchaudio as ta
from torch.nn import functional as F
from .audio import convert_audio_channels
from .compressed import get_musdb_tracks
MIXTURE = "mixture"
EXT = ".wav"
def _track_metadata(track, sources):
track_length = None
track_samplerate = None
for source in sources + [MIXTURE]:
file = track / f"{source}{EXT}"
info = ta.info(str(file))
length = info.num_frames
if track_length is None:
track_length = length
track_samplerate = info.sample_rate
elif track_length != length:
raise ValueError(
f"Invalid length for file {file}: "
f"expecting {track_length} but got {length}.")
elif info.sample_rate != track_samplerate:
raise ValueError(
f"Invalid sample rate for file {file}: "
f"expecting {track_samplerate} but got {info.sample_rate}.")
if source == MIXTURE:
wav, _ = ta.load(str(file))
wav = wav.mean(0)
mean = wav.mean().item()
std = wav.std().item()
return {"length": length, "mean": mean, "std": std, "samplerate": track_samplerate}
def _build_metadata(path, sources):
meta = {}
path = Path(path)
for file in path.iterdir():
meta[file.name] = _track_metadata(file, sources)
return meta
class Wavset:
def __init__(
self,
root, metadata, sources,
length=None, stride=None, normalize=True,
samplerate=44100, channels=2):
"""
Waveset (or mp3 set for that matter). Can be used to train
with arbitrary sources. Each track should be one folder inside of `path`.
The folder should contain files named `{source}.{ext}`.
Files will be grouped according to `sources` (each source is a list of
filenames).
Sample rate and channels will be converted on the fly.
`length` is the sample size to extract (in samples, not duration).
`stride` is how many samples to move by between each example.
"""
self.root = Path(root)
self.metadata = OrderedDict(metadata)
self.length = length
self.stride = stride or length
self.normalize = normalize
self.sources = sources
self.channels = channels
self.samplerate = samplerate
self.num_examples = []
for name, meta in self.metadata.items():
track_length = int(self.samplerate * meta['length'] / meta['samplerate'])
if length is None or track_length < length:
examples = 1
else:
examples = int(math.ceil((track_length - self.length) / self.stride) + 1)
self.num_examples.append(examples)
def __len__(self):
return sum(self.num_examples)
def get_file(self, name, source):
return self.root / name / f"{source}{EXT}"
def __getitem__(self, index):
for name, examples in zip(self.metadata, self.num_examples):
if index >= examples:
index -= examples
continue
meta = self.metadata[name]
num_frames = -1
offset = 0
if self.length is not None:
offset = int(math.ceil(
meta['samplerate'] * self.stride * index / self.samplerate))
num_frames = int(math.ceil(
meta['samplerate'] * self.length / self.samplerate))
wavs = []
for source in self.sources:
file = self.get_file(name, source)
wav, _ = ta.load(str(file), frame_offset=offset, num_frames=num_frames)
wav = convert_audio_channels(wav, self.channels)
wavs.append(wav)
example = th.stack(wavs)
example = julius.resample_frac(example, meta['samplerate'], self.samplerate)
if self.normalize:
example = (example - meta['mean']) / meta['std']
if self.length:
example = example[..., :self.length]
example = F.pad(example, (0, self.length - example.shape[-1]))
return example
def get_wav_datasets(args, samples, sources):
sig = hashlib.sha1(str(args.wav).encode()).hexdigest()[:8]
metadata_file = args.metadata / (sig + ".json")
train_path = args.wav / "train"
valid_path = args.wav / "valid"
if not metadata_file.is_file() and args.rank == 0:
train = _build_metadata(train_path, sources)
valid = _build_metadata(valid_path, sources)
json.dump([train, valid], open(metadata_file, "w"))
if args.world_size > 1:
distributed.barrier()
train, valid = json.load(open(metadata_file))
train_set = Wavset(train_path, train, sources,
length=samples, stride=args.data_stride,
samplerate=args.samplerate, channels=args.audio_channels,
normalize=args.norm_wav)
valid_set = Wavset(valid_path, valid, [MIXTURE] + sources,
samplerate=args.samplerate, channels=args.audio_channels,
normalize=args.norm_wav)
return train_set, valid_set
def get_musdb_wav_datasets(args, samples, sources):
metadata_file = args.metadata / "musdb_wav.json"
root = args.musdb / "train"
if not metadata_file.is_file() and args.rank == 0:
metadata = _build_metadata(root, sources)
json.dump(metadata, open(metadata_file, "w"))
if args.world_size > 1:
distributed.barrier()
metadata = json.load(open(metadata_file))
train_tracks = get_musdb_tracks(args.musdb, is_wav=True, subsets=["train"], split="train")
metadata_train = {name: meta for name, meta in metadata.items() if name in train_tracks}
metadata_valid = {name: meta for name, meta in metadata.items() if name not in train_tracks}
train_set = Wavset(root, metadata_train, sources,
length=samples, stride=args.data_stride,
samplerate=args.samplerate, channels=args.audio_channels,
normalize=args.norm_wav)
valid_set = Wavset(root, metadata_valid, [MIXTURE] + sources,
samplerate=args.samplerate, channels=args.audio_channels,
normalize=args.norm_wav)
return train_set, valid_set
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