grimes / grimes.py
AlekseyKorshuk's picture
huggingartists
3b2db13
# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Lyrics dataset parsed from Genius"""
import csv
import json
import os
import gzip
import datasets
_CITATION = """\
@InProceedings{huggingartists:dataset,
title = {Lyrics dataset},
author={Aleksey Korshuk
},
year={2021}
}
"""
_DESCRIPTION = """\
This dataset is designed to generate lyrics with HuggingArtists.
"""
# Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/AlekseyKorshuk/huggingartists"
# Add the licence for the dataset here if you can find it
_LICENSE = "All rights belong to copyright holders"
_URL = "https://huggingface.co/datasets/huggingartists/grimes/resolve/main/datasets.json"
# Name of the dataset
class LyricsDataset(datasets.GeneratorBasedBuilder):
"""Lyrics dataset"""
VERSION = datasets.Version("1.0.0")
def _info(self):
# This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
features = datasets.Features(
{
"text": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
data_dir = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir,
"split": "train",
},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples as (key, example) tuples."""
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for id, pred in enumerate(data[split]):
yield id, {"text": pred}