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bloom_speech.py

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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