Datasets:
Tasks:
Translation
| # coding=utf-8 | |
| # Copyright 2021 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. | |
| """ NLPGhana Voice Dataset""" | |
| from __future__ import absolute_import, division, print_function | |
| import os | |
| import datasets | |
| #_DATA_URL = "https://zenodo.org/record/4641533/files/ak.tar.gz?download=1" | |
| #_DATA_URL = 'https://www.dropbox.com/s/o6k13voiy8kdhhk/ak.tar.gz?dl=1' | |
| _DATA_URL = "ak.tar.gz" | |
| _CITATION = """\ | |
| """ | |
| _DESCRIPTION = """\ | |
| This work is comprised of audio data of Twi, a low resourced language spoken by the Akan people in Ghana. | |
| This has been adapted by NLPGhana. | |
| """ | |
| _HOMEPAGE = "https://ghananlp.org/" | |
| _LICENSE = "" | |
| _LANGUAGES = { | |
| "ak": { | |
| "Language": "Twi", | |
| "Date": "2023-07-08", | |
| "Size": "753 MB", | |
| "Version": "tw_05_2023-07-08", | |
| }, | |
| } | |
| class NLPGhanaVoiceConfig(datasets.BuilderConfig): | |
| """BuilderConfig for NLPGhana.""" | |
| def __init__(self, name, sub_version, **kwargs): | |
| """ | |
| Args: | |
| data_dir: `string`, the path to the folder containing the files in the | |
| downloaded .tar | |
| citation: `string`, citation for the data set | |
| url: `string`, url for information about the data set | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| self.sub_version = sub_version | |
| self.language = kwargs.pop("language", None) | |
| self.date_of_snapshot = kwargs.pop("date", None) | |
| self.size = kwargs.pop("size", None) | |
| description = f"NLPGhana speech to text dataset in {self.language} version {self.sub_version} of {self.date_of_snapshot}. The dataset has a size of {self.size}" | |
| super(NLPGhanaVoiceConfig, self).__init__( | |
| name=name, version=datasets.Version("1.0.5", ""), description=description, **kwargs | |
| ) | |
| class NLPGhanaVoice(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| NLPGhanaVoiceConfig( | |
| name=lang_id, | |
| language=_LANGUAGES[lang_id]["Language"], | |
| sub_version=_LANGUAGES[lang_id]["Version"], | |
| date=_LANGUAGES[lang_id]["Date"], | |
| size=_LANGUAGES[lang_id]["Size"], | |
| ) | |
| for lang_id in _LANGUAGES.keys() | |
| ] | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "user_id": datasets.Value("string"), | |
| "path": datasets.Value("string"), | |
| "text": datasets.Value("string"), | |
| "durationMsec": datasets.Value("int64"), | |
| "sampleRate": datasets.Value("int64"), | |
| "speaker_gender": datasets.Value("string"), | |
| "mother_tongue": datasets.Value("string"), | |
| "date": datasets.Value("string"), | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=None, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| dl_path = dl_manager.download_and_extract(_DATA_URL) | |
| abs_path_to_data = os.path.join(dl_path, self.config.name) | |
| abs_path_to_clips = os.path.join(abs_path_to_data, "clips") | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": os.path.join(abs_path_to_data, "train.tsv"), | |
| "path_to_clips": abs_path_to_clips, | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "filepath": os.path.join(abs_path_to_data, "test.tsv"), | |
| "path_to_clips": abs_path_to_clips, | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "filepath": os.path.join(abs_path_to_data, "validation.tsv"), | |
| "path_to_clips": abs_path_to_clips, | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath, path_to_clips): | |
| """ Yields examples. """ | |
| data_fields = list(self._info().features.keys()) | |
| path_idx = data_fields.index("path") | |
| with open(filepath, encoding="utf-8") as f: | |
| lines = f.readlines() | |
| headline = lines[0] | |
| column_names = headline.strip().split("\t") | |
| assert ( | |
| column_names == data_fields | |
| ), f"The file should have {data_fields} as column names, but has {column_names}" | |
| for id_, line in enumerate(lines[1:]): | |
| field_values = line.strip().split("\t") | |
| # set absolute path for mp3 audio file | |
| field_values[path_idx] = os.path.join(path_to_clips, field_values[path_idx]) | |
| # if data is incomplete, fill with empty values | |
| if len(field_values) < len(data_fields): | |
| field_values += (len(data_fields) - len(field_values)) * ["''"] | |
| yield id_, {key: value for key, value in zip(data_fields, field_values)} |