bjelkenhed
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Browse files- README.md +19 -0
- babelbox_voice.py +127 -0
README.md
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annotations_creators:
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- crowdsourced
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language:
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- sv-SE
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language_creators:
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- crowdsourced
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license:
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- cc0-1.0
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multilinguality:
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- monolingual
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pretty_name: Babelbox Voice
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size_categories:
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- 100K<n<1M
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source_datasets: []
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tags:
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- NST
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task_categories:
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- automatic-speech-recognition
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task_ids: []
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babelbox_voice.py
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""" Babelbox Voice Dataset"""
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import csv
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import os
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import urllib
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import datasets
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import requests
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import glob
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import gzip
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from typing import List
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from datasets.utils.py_utils import size_str
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logger = datasets.logging.get_logger(__name__)
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import torchaudio
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import torch
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from tqdm import tqdm
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_CITATION = """\
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@inproceedings{babelboxvoice:2022,
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author = {Andersson, O. and Bjelkenhed, M. and Bielsa, M. et al},
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title = {Babelbox Voice: A Speech Corpus for training Whisper},
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year = 2022
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}
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"""
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class BabelboxVoiceConfig(datasets.BuilderConfig):
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"""BuilderConfig for BabelboxVoice."""
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def __init__(self, name, version, **kwargs):
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self.name = name
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self.version = version
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self.features = kwargs.pop("features", None)
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self.description = kwargs.pop("description", None)
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self.archive_url = kwargs.pop("archive_url", None)
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self.meta_url = kwargs.pop("meta_url", None)
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description = (
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f"Babelbox Voice speech to text dataset."
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)
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super(BabelboxVoiceConfig, self).__init__(
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name=name,
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version=version,
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**kwargs,
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)
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class BabelboxVoice(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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BUILDER_CONFIGS = [
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BabelboxVoiceConfig(
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name="nst",
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version=VERSION,
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description="This part of Pandora Voice includes data from National Library of Norway",
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features=["path", "audio", "sentence"],
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archive_url="/home/jovyan/shared-data/data/nst/archive",
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meta_url="/home/jovyan/shared-data/data/nst/NST_se.csv"
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)
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]
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DEFAULT_CONFIG_NAME = "nst"
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def _info(self):
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description = (
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"Babelbox Voice is an initiative to help teach machines how real people speak. "
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)
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if self.config.name == "nst":
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features = datasets.Features(
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{
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"path": datasets.Value("string"),
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"audio": datasets.features.Audio(sampling_rate=16_000),
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"sentence": datasets.Value("string")
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}
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)
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return datasets.DatasetInfo(
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description=description,
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features=features,
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supervised_keys=None,
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version=self.config.version
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)
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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archive_dir="/home/jovyan/shared-data/data/nst/archive"
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archive_files = sorted(glob.glob(archive_dir + '/**.tar.gz'), reverse=False)
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archive_paths = dl_manager.download(archive_files)
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local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
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meta_url = self.config.meta_url
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meta_path = dl_manager.download_and_extract(meta_url)
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metadata = {}
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with open(meta_path, encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for row in tqdm(reader, desc="Reading metadata..."):
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filename = row['filename_channel_1']
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sentence = row['text']
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metadata[filename] = sentence
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN,
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gen_kwargs={
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"local_extracted_archive_paths": local_extracted_archive_paths,
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"archives": [dl_manager.iter_archive(path) for path in archive_paths],
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"metadata": metadata
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})
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]
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def _generate_examples(self, local_extracted_archive_paths, archives, metadata):
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sampling_rate = 16000
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for i, audio_archive in enumerate(archives):
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for path, file in audio_archive:
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if local_extracted_archive_paths == False:
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path = os.path.join(local_extracted_archive_paths[i], path)
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result = dict()
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result["path"] = path
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result["audio"] = {"path": path, "bytes": file.read()}
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result["sentence"] = metadata[path]
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yield path, result
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