| """ Babelbox Voice Dataset""" | |
| import csv | |
| import os | |
| import urllib | |
| import datasets | |
| import requests | |
| import glob | |
| import gzip | |
| from typing import List | |
| from datasets.utils.py_utils import size_str | |
| logger = datasets.logging.get_logger(__name__) | |
| import torchaudio | |
| import torch | |
| from tqdm import tqdm | |
| _CITATION = """\ | |
| @inproceedings{babelboxvoice:2022, | |
| author = {Andersson, O. and Bjelkenhed, M. and Bielsa, M. et al}, | |
| title = {Babelbox Voice: A Speech Corpus for training Whisper}, | |
| year = 2022 | |
| } | |
| """ | |
| class BabelboxVoiceConfig(datasets.BuilderConfig): | |
| """BuilderConfig for BabelboxVoice.""" | |
| def __init__(self, name, version, **kwargs): | |
| self.name = name | |
| self.version = version | |
| self.features = kwargs.pop("features", None) | |
| self.description = kwargs.pop("description", None) | |
| self.archive_url = kwargs.pop("archive_url", None) | |
| self.meta_url = kwargs.pop("meta_url", None) | |
| description = ( | |
| f"Babelbox Voice speech to text dataset." | |
| ) | |
| super(BabelboxVoiceConfig, self).__init__( | |
| name=name, | |
| version=version, | |
| **kwargs, | |
| ) | |
| class BabelboxVoice(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("1.0.0") | |
| BUILDER_CONFIGS = [ | |
| BabelboxVoiceConfig( | |
| name="nst", | |
| version=VERSION, | |
| description="This part of Pandora Voice includes data from National Library of Norway", | |
| features=["path", "audio", "sentence"], | |
| archive_url="/home/jovyan/shared-data/data/nst/archive", | |
| meta_url="/home/jovyan/shared-data/data/nst/NST_se.csv" | |
| ) | |
| ] | |
| DEFAULT_CONFIG_NAME = "nst" | |
| def _info(self): | |
| description = ( | |
| "Babelbox Voice is an initiative to help teach machines how real people speak. " | |
| ) | |
| if self.config.name == "nst": | |
| features = datasets.Features( | |
| { | |
| "path": datasets.Value("string"), | |
| "audio": datasets.features.Audio(sampling_rate=16_000), | |
| "sentence": datasets.Value("string") | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=description, | |
| features=features, | |
| supervised_keys=None, | |
| version=self.config.version | |
| ) | |
| def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: | |
| archive_dir="/home/jovyan/shared-data/data/nst/archive" | |
| archive_files = sorted(glob.glob(archive_dir + '/**.tar.gz'), reverse=False) | |
| archive_paths = dl_manager.download(archive_files) | |
| local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {} | |
| meta_url = self.config.meta_url | |
| meta_path = dl_manager.download_and_extract(meta_url) | |
| metadata = {} | |
| with open(meta_path, encoding="utf-8") as f: | |
| reader = csv.DictReader(f) | |
| for row in tqdm(reader, desc="Reading metadata..."): | |
| filename = row['filename_channel_1'] | |
| sentence = row['text'] | |
| metadata[filename] = sentence | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "local_extracted_archive_paths": local_extracted_archive_paths, | |
| "archives": [dl_manager.iter_archive(path) for path in archive_paths], | |
| "metadata": metadata | |
| }) | |
| ] | |
| def _generate_examples(self, local_extracted_archive_paths, archives, metadata): | |
| sampling_rate = 16000 | |
| for i, audio_archive in enumerate(archives): | |
| for path, file in audio_archive: | |
| if local_extracted_archive_paths == False: | |
| path = os.path.join(local_extracted_archive_paths[i], path) | |
| result = dict() | |
| result["path"] = path | |
| result["audio"] = {"path": path, "bytes": file.read()} | |
| result["sentence"] = metadata[path] | |
| yield path, result | |