# coding=utf-8 # Copyright 2022 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. """ LibriVox-Indonesia Dataset""" import csv import os import datasets from datasets.utils.py_utils import size_str from .languages import LANGUAGES from .release_stats import STATS _CITATION = """\ """ _HOMEPAGE = "https://huggingface.co/indonesian-nlp/librivox-indonesia" _LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/" _AUDIO_URL = "https://huggingface.co/datasets/cahya/test01/resolve/main/audio.tgz" class Test01Config(datasets.BuilderConfig): """BuilderConfig for Test01.""" def __init__(self, name, version, **kwargs): print("## Kwargs", kwargs) self.language = kwargs.pop("language", None) self.release_date = kwargs.pop("release_date", None) self.num_clips = kwargs.pop("num_clips", None) self.num_speakers = kwargs.pop("num_speakers", None) self.total_hr = kwargs.pop("total_hr", None) self.size_bytes = kwargs.pop("size_bytes", None) self.size_human = size_str(self.size_bytes) description = ( f"LibriVox-Indonesia speech to text dataset in {self.language} released on {self.release_date}. " f"The dataset comprises {self.total_hr} hours of transcribed speech data" ) super(Test01Config, self).__init__( name=name, version=datasets.Version(version), description=description, **kwargs, ) class Test01(datasets.GeneratorBasedBuilder): DEFAULT_CONFIG_NAME = "all" BUILDER_CONFIGS = [ Test01Config( name=lang, version=STATS["version"], language=LANGUAGES[lang], release_date=STATS["date"], num_clips=lang_stats["clips"], num_speakers=lang_stats["users"], total_hr=float(lang_stats["totalHrs"]) if lang_stats["totalHrs"] else None, size_bytes=int(lang_stats["size"]) if lang_stats["size"] else None, ) for lang, lang_stats in STATS["locales"].items() ] def _info(self): total_languages = len(STATS["locales"]) total_hours = self.config.total_hr description = ( "LibriVox-Indonesia is a speech dataset generated from LibriVox with only languages from Indonesia." f"The dataset currently consists of {total_hours} hours of speech " f" in {total_languages} languages, but more voices and languages are always added." ) features = datasets.Features( { "path": datasets.Value("string"), "language": datasets.Value("string"), "reader": datasets.Value("string"), "sentence": datasets.Value("string"), "audio": datasets.features.Audio(sampling_rate=48_000) } ) return datasets.DatasetInfo( description=description, features=features, supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, version=self.config.version, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" dl_manager.download_config.ignore_url_params = True archive_path = dl_manager.download(_AUDIO_URL) local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else None path_to_clips = "audio" return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "local_extracted_archive": local_extracted_archive, "archive_iterator": dl_manager.iter_archive(archive_path), "metadata_filepath": "audio_transcription.csv", "path_to_clips": path_to_clips, }, ), ] def _generate_examples( self, local_extracted_archive, archive_iterator, metadata_filepath, path_to_clips, ): """Yields examples.""" data_fields = list(self._info().features.keys()) metadata = {} filepath = local_extracted_archive + "/audio/audio_transcription.csv" with open(filepath, "r") as f: lines = (line for line in f) utterances = csv.DictReader(lines) for row in utterances: if self.config.name == "all" or self.config.name == row["language"]: row["path"] = os.path.join(path_to_clips, row["path"]) # if data is incomplete, fill with empty values for field in data_fields: if field not in row: row[field] = "" metadata[row["path"]] = row for path, f in archive_iterator: if path in metadata: result = dict(metadata[path]) # set the audio feature and the path to the extracted file path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path result["audio"] = {"path": path, "bytes": f.read()} # set path to None if the audio file doesn't exist locally (i.e. in streaming mode) result["path"] = path if local_extracted_archive else None yield path, result