musicbrainz_a1 / musicbrainz_a1.py
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Update musicbrainz_a1.py
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import datasets
import json
import numpy
import tarfile
import io
from io import BytesIO
CHUNK_COUNT = 330
_FEATURES = datasets.Features(
{
"mbid": datasets.Value("string"),
"title": datasets.Value("string"),
"artists": datasets.Value("string"),
"isrc": datasets.Value("string"),
"length": datasets.Value("string"),
"date": datasets.Value("string"),
"ytid": datasets.Value("string"),
}
)
class MusicBrainzLoaderStream(datasets.GeneratorBasedBuilder):
"""MusicBrainz Dataset"""
def _info(self):
return datasets.DatasetInfo(
description="MusicBrainz Dataset",
features=_FEATURES,
homepage="None",
citation="None",
license="None"
)
def _split_generators(self, dl_manager):
# Load the chunk list.
_CHUNK_LIST = [x for x in range(CHUNK_COUNT)]
# Create a list to hold the downloaded chunks.
_list = []
# Download each chunk file.
for chunk in _CHUNK_LIST:
_list.append(dl_manager.download(f"data/{chunk+1}.jsonl"))
# Return the list of downloaded chunks.
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"chunks": _list,
},
),
]
def _generate_examples(self, chunks):
"""Generate images and labels for splits."""
for chunk in chunks:
with open(chunk, mode='r') as infile:
for line in infile:
mbid, title, artists, isrc, length, date, ytid = json.loads(line)
yield mbid, {
"mbid": mbid,
"title": title,
"artists": json.dumps(artists),
"isrc": json.dumps(isrc),
"length": length,
"date": date,
"ytid": ytid
}