bill-wurtz / bill-wurtz.py
1 # coding=utf-8
2 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
3 #
4 # Licensed under the Apache License, Version 2.0 (the "License");
5 # you may not use this file except in compliance with the License.
6 # You may obtain a copy of the License at
7 #
8 # http://www.apache.org/licenses/LICENSE-2.0
9 #
10 # Unless required by applicable law or agreed to in writing, software
11 # distributed under the License is distributed on an "AS IS" BASIS,
12 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 # See the License for the specific language governing permissions and
14 # limitations under the License.
15 """Lyrics dataset parsed from Genius"""
16
17
18 import csv
19 import json
20 import os
21 import gzip
22
23 import datasets
24
25
26 _CITATION = """\
27 @InProceedings{huggingartists:dataset,
28 title = {Lyrics dataset},
29 author={Aleksey Korshuk
30 },
31 year={2021}
32 }
33 """
34
35
36 _DESCRIPTION = """\
37 This dataset is designed to generate lyrics with HuggingArtists.
38 """
39
40 # Add a link to an official homepage for the dataset here
41 _HOMEPAGE = "https://github.com/AlekseyKorshuk/huggingartists"
42
43 # Add the licence for the dataset here if you can find it
44 _LICENSE = "All rights belong to copyright holders"
45
46 _URL = "https://huggingface.co/datasets/huggingartists/bill-wurtz/resolve/main/datasets.json"
47
48 # Name of the dataset
49 class LyricsDataset(datasets.GeneratorBasedBuilder):
50 """Lyrics dataset"""
51
52 VERSION = datasets.Version("1.0.0")
53
54 def _info(self):
55 # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
56 features = datasets.Features(
57 {
58 "text": datasets.Value("string"),
59 }
60 )
61 return datasets.DatasetInfo(
62 # This is the description that will appear on the datasets page.
63 description=_DESCRIPTION,
64 # This defines the different columns of the dataset and their types
65 features=features, # Here we define them above because they are different between the two configurations
66 # If there's a common (input, target) tuple from the features,
67 # specify them here. They'll be used if as_supervised=True in
68 # builder.as_dataset.
69 supervised_keys=None,
70 # Homepage of the dataset for documentation
71 homepage=_HOMEPAGE,
72 # License for the dataset if available
73 license=_LICENSE,
74 # Citation for the dataset
75 citation=_CITATION,
76 )
77
78 def _split_generators(self, dl_manager):
79 """Returns SplitGenerators."""
80 # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
81 # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
82
83 # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
84 # 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.
85 # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
86
87 data_dir = dl_manager.download_and_extract(_URL)
88 return [
89 datasets.SplitGenerator(
90 name=datasets.Split.TRAIN,
91 # These kwargs will be passed to _generate_examples
92 gen_kwargs={
93 "filepath": data_dir,
94 "split": "train",
95 },
96 ),
97 ]
98
99
100 def _generate_examples(self, filepath, split):
101 """Yields examples as (key, example) tuples."""
102 # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
103
104 with open(filepath, encoding="utf-8") as f:
105 data = json.load(f)
106 for id, pred in enumerate(data[split]):
107 yield id, {"text": pred}