Datasets:
Size:
10K<n<100K
ArXiv:
| from pathlib import Path | |
| import datasets | |
| from datasets import ( | |
| Features, | |
| SplitGenerator, | |
| ) | |
| import pandas as pd | |
| DATA_ARCHIVE = "data.zip" | |
| TABLE_ARCHIVE = "tables.zip" | |
| NAMES = [ | |
| "all", | |
| "asm", | |
| "bgc", | |
| "bht", | |
| "ckb", | |
| "ewe", | |
| "guj", | |
| "ibo", | |
| "kan", | |
| "lin", | |
| "luo", | |
| "mal", | |
| "mar", | |
| "nag", | |
| "nde", | |
| "nlx", | |
| "pan", | |
| "peg", | |
| "tam", | |
| "tel", | |
| "tw-akuapem", | |
| "tw-asante", | |
| "ukr", | |
| "urd", | |
| "vie", | |
| "yor", | |
| ] | |
| DESCRIPTION = """""" | |
| CITATION = """@misc{keller2024speechtaximultilingualsemanticspeech, | |
| title={SpeechTaxi: On Multilingual Semantic Speech Classification}, | |
| author={Lennart Keller and Goran Glavaš}, | |
| year={2024}, | |
| eprint={2409.06372}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL}, | |
| url={https://arxiv.org/abs/2409.06372}, | |
| }""" | |
| HOMPAGE = "https://huggingface.co/datasets/LennartKeller/SpeechTaxi" | |
| class SpeechTaxiConfig(datasets.BuilderConfig): | |
| def __init__( | |
| self, name, description, citation, homepage | |
| ): | |
| super().__init__( | |
| name=name, | |
| version=datasets.Version("0.0.1"), | |
| description=description, | |
| ) | |
| self.name = name | |
| self.description = description | |
| self.citation = citation | |
| self.homepage = homepage | |
| def get_config(name): | |
| return SpeechTaxiConfig(name=name, description=DESCRIPTION, citation=CITATION, homepage=HOMPAGE) | |
| class SpeechTaxi(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [get_config(name) for name in NAMES] | |
| BUILDER_CONFIG_CLASS = SpeechTaxiConfig | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=DESCRIPTION, | |
| features=Features( | |
| { | |
| "verse_ref": datasets.features.Value("string"), | |
| "text_en": datasets.features.Value("string"), | |
| "language": datasets.features.Value("string"), | |
| "transcription": datasets.features.Value("string"), | |
| "transcription_romanized": datasets.features.Value("string"), | |
| "label": datasets.features.ClassLabel( | |
| names=[ | |
| "Recommendation", | |
| "Faith", | |
| "Description", | |
| "Sin", | |
| "Grace", | |
| "Violence", | |
| ] | |
| ), | |
| "audio": datasets.features.Audio(sampling_rate=16_000), | |
| "transcription_mms-zeroshot-300m": datasets.features.Value("string"), | |
| "transcription_whisper-large-v3": datasets.features.Value("string"), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage=HOMPAGE, | |
| citation=CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| table_dir = dl_manager.download_and_extract(TABLE_ARCHIVE) | |
| # Make sure data is downloaded | |
| data_dir = dl_manager.download_and_extract(DATA_ARCHIVE) | |
| return [ | |
| SplitGenerator(name="train", gen_kwargs={"split": "train", "table_dir": table_dir, "data_dir": data_dir}), | |
| SplitGenerator(name="test", gen_kwargs={"split": "test", "table_dir": table_dir, "data_dir": data_dir}), | |
| SplitGenerator(name="dev", gen_kwargs={"split": "dev", "table_dir": table_dir, "data_dir": data_dir}), | |
| # Add more splits as necessary | |
| ] | |
| def _generate_examples(self, split, table_dir, data_dir): | |
| table_dir = Path(table_dir) | |
| data_dir = Path(data_dir) | |
| idx = 0 | |
| if self.config.name != "all": | |
| table_files = [table_dir / f"{self.config.name}.tsv"] | |
| else: | |
| table_files = sorted(table_dir.glob("*.tsv")) | |
| for table_file in table_files: | |
| # Load the table | |
| df = pd.read_table(table_file) | |
| df["language"] = table_file.stem | |
| df = df.query("split == @split").reset_index(drop=True) | |
| for _, row in df.iterrows(): | |
| row["audio"] = str(data_dir / row["audio"]) | |
| yield idx, row.to_dict() | |
| idx += 1 | |