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import os
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks
_DATASETNAME = "medisco"
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_LOCAL = False
_CITATION = """\
@INPROCEEDINGS{8629259,
author={Qorib, Muhammad Reza and Adriani, Mirna},
booktitle={2018 International Conference on Asian Language Processing (IALP)},
title={Building MEDISCO: Indonesian Speech Corpus for Medical Domain},
year={2018},
volume={},
number={},
pages={133-138},
keywords={Training;Automatic speech recognition;Medical services;Writing;Buildings;Computer science;Indonesian Automatic Speech Recognition;Medical Speech Corpus;Text Corpus},
doi={10.1109/IALP.2018.8629259}
}
"""
_DESCRIPTION = "MEDISCO is a medical Indonesian speech corpus that contains 731 medical terms and consists of 4,680 utterances with total duration 10 hours"
_HOMEPAGE = "https://mrqorib.github.io/2018/02/01/building-medisco.html"
_LICENSE = "GNU General Public License v3.0 (gpl-3.0)"
_URLs = {
"medisco": {
"train": {
"audio": "https://huggingface.co/datasets/mrqorib/MEDISCO/resolve/main/MEDISCO/train/audio.tar.gz",
"text": "https://huggingface.co/datasets/mrqorib/MEDISCO/resolve/main/MEDISCO/train/annotation/sentences.txt",
},
"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"},
}
}
_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class Medisco(datasets.GeneratorBasedBuilder):
"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 = [
SEACrowdConfig(
name="medisco_source",
version=datasets.Version(_SOURCE_VERSION),
description="MEDISCO source schema",
schema="source",
subset_id="medisco",
),
SEACrowdConfig(
name="medisco_seacrowd_sptext",
version=datasets.Version(_SEACROWD_VERSION),
description="MEDISCO seacrowd schema",
schema="seacrowd_sptext",
subset_id="medisco",
),
]
DEFAULT_CONFIG_NAME = "medisco_source"
def _info(self):
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"speaker_id": datasets.Value("string"),
"path": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=44_100),
"text": datasets.Value("string"),
}
)
elif self.config.schema == "seacrowd_sptext":
features = schemas.speech_text_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
task_templates=[datasets.AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
base_path = _URLs["medisco"]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
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"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
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"},
),
]
def _generate_examples(self, filepath: Path, text_path: Path, split: str) -> Tuple[int, Dict]:
with open(text_path, encoding="utf-8") as f:
texts = f.readlines() # contains trailing \n
for speaker_id in os.listdir(filepath):
speaker_path = os.path.join(filepath, speaker_id)
if not os.path.isdir(speaker_path):
continue
for audio_id in os.listdir(speaker_path):
audio_idx = int(audio_id.split(".", 1)[0]) - 1 # get 0-based index
audio_path = os.path.join(speaker_path, audio_id)
key = "{}_{}_{}".format(split, speaker_id, audio_idx)
example = {
"id": key,
"speaker_id": speaker_id,
"path": audio_path,
"audio": audio_path,
"text": texts[audio_idx].strip(),
}
if self.config.schema == "seacrowd_sptext":
gender = speaker_id.split("-", 1)[0]
example["metadata"] = {
"speaker_gender": gender,
"speaker_age": None,
}
yield key, example
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