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"""
SEA Crowd Data Loader for Bloom Speech.
"""
from typing import Dict, List, Tuple
import datasets
from datasets.download.download_manager import DownloadManager
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import TASK_TO_SCHEMA, Licenses, Tasks
_CITATION = r"""
@inproceedings{leong-etal-2022-bloom,
title = "Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks",
author = "Leong, Colin and
Nemecek, Joshua and
Mansdorfer, Jacob and
Filighera, Anna and
Owodunni, Abraham and
Whitenack, Daniel",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.590",
doi = "10.18653/v1/2022.emnlp-main.590",
pages = "8608--8621",
}
"""
logger = datasets.logging.get_logger(__name__)
# this config is created for SEACrowd Dataloader
_LANG_CONFIG = {"bjn": "Banjar", "bzi": "Bisu", "ceb": "Cebuano", "ind": "Indonesian", "jra": "Jarai", "kqr": "Kimaragang", "mya": "Burmese", "tgl": "Tagalog"}
_LOCAL = False
_LANGUAGES = list(_LANG_CONFIG.keys())
_DATASETNAME = "bloom_speech"
_DESCRIPTION = r"""
This version of the Bloom Library data is developed specifically for the automatic speech recognition and speech-to-text tasks.
It includes data from 56 languages across 18 language families. 8 languages are spoken in Southeast Asia.
Before using this dataloader, please accept the acknowledgement at https://huggingface.co/datasets/sil-ai/bloom-speech and use huggingface-cli login for authentication.
"""
_HOMEPAGE = "https://huggingface.co/datasets/sil-ai/bloom-speech"
_LICENSE = Licenses.CC.value
_URL = "https://huggingface.co/datasets/sil-ai/bloom-speech"
_HF_REMOTE_REF = "/".join(_URL.split("/")[-2:])
_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
_SOURCE_VERSION = "0.0.1"
_SEACROWD_VERSION = "2024.06.20"
CONFIG_SUFFIXES_FOR_TASK = [TASK_TO_SCHEMA.get(task).lower() for task in _SUPPORTED_TASKS]
def construct_configs_on_langs(languages: list = None) -> List[SEACrowdConfig]:
"""
The function `construct_configs` constructs a list of SEACrowdConfig objects based on the provided
languages or a default language, and returns the list.
input:
languages (list, default None): The `languages` parameter is a list that specifies the languages for which the
configurations need to be constructed. If no languages are provided (value=None), the first value in language config
will be used.
output:
a list of `SEACrowdConfig` objects based on instantiated init variables
"""
# set output var
config_list = []
# construct zipped arg for config instantiation
TASKS_AND_CONFIG_SUFFIX_PAIRS = list(zip(_SUPPORTED_TASKS, CONFIG_SUFFIXES_FOR_TASK))
# implement source schema
version, config_name_prefix = _SOURCE_VERSION, "source"
config_list += [
SEACrowdConfig(
name=f"{_DATASETNAME}_{_LANG}_{config_name_prefix}",
version=datasets.Version(version),
description=f"{_DATASETNAME} {config_name_prefix} schema for language code {_LANG}",
schema=f"{config_name_prefix}",
subset_id=_LANG,
)
for _LANG in languages
]
# implement SEACrowd schema
version, config_name_prefix = _SEACROWD_VERSION, "seacrowd"
for task_obj, config_name_suffix in TASKS_AND_CONFIG_SUFFIX_PAIRS:
config_list += [
SEACrowdConfig(
name=f"{_DATASETNAME}_{_LANG}_{config_name_prefix}_{config_name_suffix}",
version=datasets.Version(version),
description=f"{_DATASETNAME} {config_name_prefix} schema for {task_obj.name} and language code {_LANG}",
schema=f"{config_name_prefix}_{config_name_suffix}",
subset_id=_LANG,
)
for _LANG in languages
]
return config_list
class BloomSpeechDataset(datasets.GeneratorBasedBuilder):
"""Bloom Speech dataset, subsetted from https://huggingface.co/datasets/sil-ai/bloom-speech"""
# get all schema w/o lang arg + get all schema w/ lang arg
BUILDER_CONFIGS = construct_configs_on_langs(_LANGUAGES)
def _info(self) -> datasets.DatasetInfo:
_config_schema_name = self.config.schema
logger.info(f"Received schema name: {self.config.schema}")
# source schema
if _config_schema_name == "source":
features = datasets.Features(
{
"file": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
"text": datasets.Value("string"),
"book": datasets.Value("string"),
"instance": datasets.Value("string"),
"license": datasets.Value("string"),
"credits": datasets.Value("string"),
"original_lang_tag": datasets.Value("string"),
}
)
# speech-text schema
elif _config_schema_name == "seacrowd_sptext":
features = schemas.speech_text_features
else:
raise ValueError(f"Received unexpected config schema of {_config_schema_name}!")
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
hf_dset_dict = datasets.load_dataset(_HF_REMOTE_REF, self.config.subset_id)
return [datasets.SplitGenerator(name=datasets.Split(dset_key), gen_kwargs={"hf_dset": dset}) for dset_key, dset in hf_dset_dict.items() if dset.num_rows > 0]
def _generate_examples(self, hf_dset) -> Tuple[int, Dict]:
_config_schema_name = self.config.schema
_idx = 0
for datapoints in hf_dset:
# since no _idx is available to be used, we're creating it manually for both schema
if _config_schema_name == "source":
yield _idx, {colname: datapoints[colname] for colname in self.info.features}
elif _config_schema_name == "seacrowd_sptext":
yield _idx, {"id": _idx, "path": datapoints["file"], "audio": datapoints["audio"], "text": datapoints["text"], "speaker_id": None, "metadata": {"speaker_age": None, "speaker_gender": None}}
else:
raise ValueError(f"Received unexpected config schema of {_config_schema_name}!")
_idx += 1
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