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!pip install pysrt""" Babelbox Voice Dataset"""
import os
import csv
import codecs
import datasets
from typing import List
from pathlib import Path
from tqdm import tqdm
logger = datasets.logging.get_logger(__name__)
_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
}
"""
_HF_REPO_PATH = "https://huggingface.co/datasets/babelbox/babelbox_voice/"
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.data_url = kwargs.pop("data_url", None)
self.nb_data_shards = kwargs.pop("nb_data_shards", None)
self.metadata_url = kwargs.pop("metadata_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 Babel Voice includes data from National Library of Norway",
features=["path", "audio", "sentence"],
data_url= _HF_REPO_PATH + "resolve/main/data/nst/nst-data-{:0>3d}.tar.gz",
nb_data_shards = 42,
metadata_url= _HF_REPO_PATH + "resolve/main/data/nst/metadata.tar.gz"
)
]
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")
}
)
else:
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 get_metadata(self, dl_manager, metadata_url):
if metadata_url == None: return None
metadata_path = dl_manager.download(metadata_url)
local_extracted_metadata_path = dl_manager.extract(metadata_path) if not dl_manager.is_streaming else None
metadata_archive = dl_manager.iter_archive(metadata_path)
metadata = {}
for path, file in metadata_archive:
reader = csv.DictReader(codecs.iterdecode(file, 'utf-8'))
for row in tqdm(reader, desc="Reading metadata..."):
filename = row['filename_channel_1']
sentence = row['text']
metadata[filename] = sentence
return metadata
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
download_urls = [self.config.data_url.format(i) for i in range(1, self.config.nb_data_shards + 1) ]
archive_paths = dl_manager.download(download_urls)
local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
metadata = self.get_metadata(dl_manager, self.config.metadata_url)
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
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