| import os
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| from pathlib import Path
|
| from typing import Dict, List, Tuple
|
|
|
| import datasets
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|
|
| from seacrowd.utils import schemas
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| from seacrowd.utils.configs import SEACrowdConfig
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| from seacrowd.utils.constants import Tasks
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|
|
| _DATASETNAME = "medisco"
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|
|
| _LANGUAGES = ["ind"]
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| _LOCAL = False
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| _CITATION = """\
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| @INPROCEEDINGS{8629259,
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| author={Qorib, Muhammad Reza and Adriani, Mirna},
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| booktitle={2018 International Conference on Asian Language Processing (IALP)},
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| title={Building MEDISCO: Indonesian Speech Corpus for Medical Domain},
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| year={2018},
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| volume={},
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| number={},
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| pages={133-138},
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| keywords={Training;Automatic speech recognition;Medical services;Writing;Buildings;Computer science;Indonesian Automatic Speech Recognition;Medical Speech Corpus;Text Corpus},
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| doi={10.1109/IALP.2018.8629259}
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| }
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| """
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|
|
| _DESCRIPTION = "MEDISCO is a medical Indonesian speech corpus that contains 731 medical terms and consists of 4,680 utterances with total duration 10 hours"
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|
|
| _HOMEPAGE = "https://mrqorib.github.io/2018/02/01/building-medisco.html"
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|
|
| _LICENSE = "GNU General Public License v3.0 (gpl-3.0)"
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|
|
| _URLs = {
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| "medisco": {
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| "train": {
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| "audio": "https://huggingface.co/datasets/mrqorib/MEDISCO/resolve/main/MEDISCO/train/audio.tar.gz",
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| "text": "https://huggingface.co/datasets/mrqorib/MEDISCO/resolve/main/MEDISCO/train/annotation/sentences.txt",
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| },
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| "test": {"audio": "https://huggingface.co/datasets/mrqorib/MEDISCO/resolve/main/MEDISCO/test/audio.tar.gz", "text": "https://huggingface.co/datasets/mrqorib/MEDISCO/resolve/main/MEDISCO/test/annotation/sentences.txt"},
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| }
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| }
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|
|
| _SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
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|
|
| _SOURCE_VERSION = "1.0.0"
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| _SEACROWD_VERSION = "2024.06.20"
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|
|
|
|
| class Medisco(datasets.GeneratorBasedBuilder):
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| "MEDISCO is a medical Indonesian speech corpus that contains 731 medical terms and consists of 4,680 utterances with total duration 10 hours"
|
|
|
| BUILDER_CONFIGS = [
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| SEACrowdConfig(
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| name="medisco_source",
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| version=datasets.Version(_SOURCE_VERSION),
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| description="MEDISCO source schema",
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| schema="source",
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| subset_id="medisco",
|
| ),
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| SEACrowdConfig(
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| name="medisco_seacrowd_sptext",
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| version=datasets.Version(_SEACROWD_VERSION),
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| description="MEDISCO seacrowd schema",
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| schema="seacrowd_sptext",
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| subset_id="medisco",
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| ),
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| ]
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|
|
| DEFAULT_CONFIG_NAME = "medisco_source"
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|
|
| def _info(self):
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| if self.config.schema == "source":
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| features = datasets.Features(
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| {
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| "id": datasets.Value("string"),
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| "speaker_id": datasets.Value("string"),
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| "path": datasets.Value("string"),
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| "audio": datasets.Audio(sampling_rate=44_100),
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| "text": datasets.Value("string"),
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| }
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| )
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| elif self.config.schema == "seacrowd_sptext":
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| features = schemas.speech_text_features
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|
|
| return datasets.DatasetInfo(
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| description=_DESCRIPTION,
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| features=features,
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| homepage=_HOMEPAGE,
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| license=_LICENSE,
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| citation=_CITATION,
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| task_templates=[datasets.AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
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| )
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|
|
| def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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| base_path = _URLs["medisco"]
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|
|
| return [
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| datasets.SplitGenerator(
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| name=datasets.Split.TRAIN,
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| gen_kwargs={"filepath": dl_manager.download_and_extract(base_path["train"]["audio"]), "text_path": dl_manager.download_and_extract(base_path["train"]["text"]), "split": "train"},
|
| ),
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| datasets.SplitGenerator(
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| name=datasets.Split.TEST,
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| gen_kwargs={"filepath": dl_manager.download_and_extract(base_path["test"]["audio"]), "text_path": dl_manager.download_and_extract(base_path["test"]["text"]), "split": "test"},
|
| ),
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| ]
|
|
|
| def _generate_examples(self, filepath: Path, text_path: Path, split: str) -> Tuple[int, Dict]:
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|
|
| with open(text_path, encoding="utf-8") as f:
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| texts = f.readlines()
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|
|
| for speaker_id in os.listdir(filepath):
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| speaker_path = os.path.join(filepath, speaker_id)
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| if not os.path.isdir(speaker_path):
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| continue
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| for audio_id in os.listdir(speaker_path):
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| audio_idx = int(audio_id.split(".", 1)[0]) - 1
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| audio_path = os.path.join(speaker_path, audio_id)
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| key = "{}_{}_{}".format(split, speaker_id, audio_idx)
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| example = {
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| "id": key,
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| "speaker_id": speaker_id,
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| "path": audio_path,
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| "audio": audio_path,
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| "text": texts[audio_idx].strip(),
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| }
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| if self.config.schema == "seacrowd_sptext":
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| gender = speaker_id.split("-", 1)[0]
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| example["metadata"] = {
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| "speaker_gender": gender,
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| "speaker_age": None,
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| }
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| yield key, example
|
|
|
