# 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: Address all TODOs and remove all explanatory comments """TODO: Add a description here.""" import csv import json import os import sys import datasets # 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. """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "" # 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 Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLS = { "voxpopuli": "https://public-dataset-model-store.awsdev.asapp.com/users/sshon/public/slue/slue-voxpopuli_v0.2_blind.zip" } # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case class SLUEVoxpopuli(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="voxpopuli", 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 == "voxpopuli": # This is the name of the configuration selected in BUILDER_CONFIGS above # get the current split features = datasets.Features( { "id": datasets.Value("string"), "raw_text": datasets.Value("string"), "normalized_text": datasets.Value("string"), "speaker_id": datasets.Value("int32"), "split": datasets.Value("string"), "raw_ner": datasets.Value("string"), "normalized_ner": datasets.Value("string"), "audio_path": 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, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # 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): # 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 urls = _URLS[self.config.name] data_dir = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "slue-voxpopuli", "slue-voxpopuli_fine-tune.tsv"), "split": "fine-tune", "audio_dir": os.path.join(data_dir, "slue-voxpopuli", "fine-tune"), }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "slue-voxpopuli", "slue-voxpopuli_dev.tsv"), "split": "dev", "audio_dir": os.path.join(data_dir, "slue-voxpopuli", "dev"), }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "slue-voxpopuli", "slue-voxpopuli_test_blind.tsv"), "split": "test-blind", "audio_dir": os.path.join(data_dir, "slue-voxpopuli", "test"), }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split, audio_dir): # read tsv file by rows with open(filepath, encoding="utf-8") as f: reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) for row in reader: speaker_id = row["speaker_id"] if not speaker_id.isdigit(): speaker_id = -1 else: speaker_id = int(speaker_id) audio_file = f"{row['id']}.ogg" if split == "test-blind": yield row["id"], { "id": row["id"], "raw_text": row["raw_text"], "normalized_text": row["normalized_text"], "speaker_id": speaker_id, "split": split, "audio_path": os.path.join(audio_dir, audio_file), "raw_ner": None, "normalized_ner": None } elif split == "fine-tune" or split == "dev": yield row["id"], { "id": row["id"], "raw_text": row["raw_text"], "normalized_text": row["normalized_text"], "speaker_id": speaker_id, "split": split, "raw_ner": row["raw_ner"], "normalized_ner": row["normalized_ner"], "audio_path": os.path.join(audio_dir, audio_file), }