|
"""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 |
|
|
