Datasets:
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"""NPSC dataset."""
import gzip
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\\nNorwegian Colossal Corpus v2. Short sequences of maximum 100k characters."""
_CITATION = """
TO BE DONE
"""
_URL = "https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-58/"
_DATA_URL = "https://huggingface.co/datasets/NbAiLab/NPSC/resolve/main/data/{split_suffix}-shard-{index:04d}-of-{n_shards:04d}.json.gz"
_N_SHARDS_PER_SPLIT = {
"train": 1, "dev": 1, "test": 1
}
class NPSCConfig(datasets.BuilderConfig):
"""BuilderConfig for NbNn."""
def __init__(self, *args, **kwargs):
"""BuilderConfig for NbNn.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super().__init__(
*args,
name="NPSC",
**kwargs,
)
class NPSC(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [NPSCConfig()]
BUILDER_CONFIG_CLASS = NPSCConfig
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"sentence_order": datasets.Value("int32"),
"speaker_id" : datasets.Value("int32"),
"speaker_name": datasets.Value("string"),
"sentence_text": datasets.Value("string"),
"sentence_language_code": datasets.Value("string"),
"text": datasets.Value("string"),
"start_time": datasets.Value("int32"),
"end_time": datasets.Value("int32"),
"normsentence_text": datasets.Value("string"),
"transsentence_text": datasets.Value("string"),
"translated": datasets.Value("int32"),
"audio": datasets.features.Audio(sampling_rate=48000),
}
),
supervised_keys=None,
homepage=_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_urls = {}
for split in ["train", "dev", "test"]:
data_urls[split] = [
_DATA_URL.format(
language=self.config.name,
split_suffix=split,
index=index,
n_shards=_N_SHARDS_PER_SPLIT[split],
)
for index in range(1, _N_SHARDS_PER_SPLIT[split] + 1)
]
train_downloaded_files = dl_manager.download(data_urls["train"])
dev_downloaded_files = dl_manager.download(data_urls["dev"])
test_downloaded_files = dl_manager.download(data_urls["test"])
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_downloaded_files}
),
]
def _generate_examples(self, filepaths):
"""This function returns the examples in the raw (text) form by iterating on all the files."""
id_ = 0
for filepath in filepaths:
logger.info("generating examples from = %s", filepath)
with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
for line in f:
if line:
example = json.loads(line)
yield id_, example
id_ += 1
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