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# Testing how to load rarfile from Zenodo, specifically https://zenodo.org/record/4661645.
# https://github.com/huggingface/datasets/blob/dfdd2f949c1840926c02ae47f0f0c43083ef0b1f/datasets/common_voice/common_voice.py#L661 provided some inspiration
# also https://huggingface.co/docs/datasets/master/dataset_script.html
# 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.
"""TODO: Add a description here."""
import csv
import json
import os
import datasets
import pandas as pd
import rarfile
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""
_HOMEPAGE = "https://zenodo.org/record/466164"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLs = {
"first_domain": "https://zenodo.org/record/4661645/files/Keyword_spotting_dataset_v0.01_17042021.rar",
}
class TempAfricaNLPKeywordSpottingForAfricanLanguages(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.1.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="first_domain",
version=VERSION,
description="This part of my dataset covers a first domain",
),
]
DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
if (
self.config.name == "first_domain"
): # This is the name of the configuration selected in BUILDER_CONFIGS above
features = datasets.Features(
{
"sentence": datasets.Value("string"),
"path": datasets.Value("string"),
"audio": datasets.features.Audio() # TODO: sampling rate? https://huggingface.co/docs/datasets/master/package_reference/main_classes.html#datasets.Audio
# TODO: 'id', 'client_id', 'path', 'sentence', 'original_sentence_id', 'created_at', 'bucket', 'locale_id'
}
)
else: # This is an example to show how to have different features for "first_domain" and "second_domain"
features = datasets.Features(
{
"sentence": datasets.Value("string"),
"option2": datasets.Value("string"),
"second_domain_answer": datasets.Value("string")
# These are the features of your dataset like images, labels ...
}
)
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."""
# TODO: 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
my_urls = _URLs[self.config.name]
data_dir = dl_manager.download_and_extract(my_urls)
data_dir = os.path.join(
data_dir, "data_17042021"
) # the rar file has a subfolder.
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "clips.xlsx"),
"split": "train",
"data_dir": data_dir,
},
),
#
# datasets.SplitGenerator(
# name=datasets.Split.TEST,
# # These kwargs will be passed to _generate_examples
# gen_kwargs={
# "filepath": os.path.join(data_dir, "test.jsonl"),
# "split": "test"
# },
# ),
# datasets.SplitGenerator(
# name=datasets.Split.VALIDATION,
# # These kwargs will be passed to _generate_examples
# gen_kwargs={
# "filepath": os.path.join(data_dir, "dev.jsonl"),
# "split": "dev",
# },
# ),
#
]
def _generate_examples(
self,
filepath,
split,
data_dir, # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
):
"""Yields examples as (key, example) tuples."""
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is here for legacy reason (tfds) and is not important in itself.
clips_df = pd.read_excel(filepath)
with open(filepath, encoding="utf-8") as f:
for id_, row in clips_df.iterrows():
data = row
bucket = row["bucket"]
if bucket == split:
yield id_, {
"sentence": data["sentence"],
"path": data["path"],
"audio": os.path.join(
data_dir, data["path"]
), # set the audio feature, should be able to handle things automatically?
}
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