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import datasets
from pathlib import Path
import stempeg
import numpy as np
_DESCRIPTION = """\
MUSDB18 music source separation dataset
to open original stem file (mp4), which is done internally you need stempeg library.
Outcome of mp4 file is a 3 dimensional np_array [n_stems, n_samples, sample_rate].
firt dimension meanings: {
0: mixture.
1: drugs,
2: bass,
3: others,
4:vocals,
}
Original dataset is not cutted in any parts, but here I cut each song in 10 seconds chunks with 1 sec overlap.
"""
_DESCRIPTION = "musdb dataset"
class Musdb18Dataset(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 300
SAMPLING_RATE = 44100
WINDOW_SIZE = SAMPLING_RATE * 10 # 10s windows
INSTRUMENT_NAMES = ["mixture", "drums", "bass", "other", "vocals"]
#! To configure different configurations (length of window is the only thing)
# use datasets.BuilderConfig
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"name": datasets.Value("string"),
"n_window": datasets.Value("int16"),
**{
name: datasets.Audio(
sampling_rate=self.SAMPLING_RATE, mono=False
)
for name in self.INSTRUMENT_NAMES
},
"mean": datasets.Value("float"),
"std": datasets.Value("float"),
}
),
)
def _split_generators(self, dl_manager):
#! you must have your folder locally!
archive_path = dl_manager.download_and_extract(
"https://zenodo.org/record/1117372/files/musdb18.zip?download=1"
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"audio_path": f"{archive_path}/train"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"audio_path": f"{archive_path}/test"},
),
]
def _generate_stem_dict(self, S, song_name, end):
return {
name: {
"path": f"{song_name}/{name}",
"array": S[i, end - self.WINDOW_SIZE : end, :],
"sampling_rate": self.SAMPLING_RATE,
}
for i, name in enumerate(self.INSTRUMENT_NAMES)
}
def _generate_examples(self, audio_path):
id_ = 0
for stems_path in Path(audio_path).iterdir():
song_name = stems_path.stem
S, sr = stempeg.read_stems(
str(stems_path), dtype=np.float32, multiprocess=False
)
mixture = S.sum(axis=0).T
assert mixture.shape[0] == 2
# from (n_instr, n_chann, n_samp) -> (n_chann, n_samp)
mixture = mixture.mean(0) # channel_wise mean -> (n_samples,)
mean = mixture.mean().item()
std = mixture.std().item()
for idx, end in enumerate(
range(self.WINDOW_SIZE, S.shape[1], self.WINDOW_SIZE)
):
yield id_, {
"name": song_name,
"n_window": idx,
**self._generate_stem_dict(S, song_name, end),
"mean": mean,
"std": std,
}
id_ += 1
# It's very rare for song to have exactly 3 minutes
yield id_, {
"name": song_name,
"n_window": idx + 1,
**self._generate_stem_dict(S, song_name, end=S.shape[1]),
"mean": mean,
"std": std,
}
id_ += 1
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