from pathlib import Path from typing import List import datasets import json import os from nusacrowd.utils import schemas from nusacrowd.utils.configs import NusantaraConfig from nusacrowd.utils.constants import Tasks, DEFAULT_SOURCE_VIEW_NAME, DEFAULT_NUSANTARA_VIEW_NAME _DATASETNAME = "titml_idn" _SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME _UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME _LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data) _LOCAL = False _CITATION = """\ @inproceedings{lestari2006titmlidn, title={A large vocabulary continuous speech recognition system for Indonesian language}, author={Lestari, Dessi Puji and Iwano, Koji and Furui, Sadaoki}, booktitle={15th Indonesian Scientific Conference in Japan Proceedings}, pages={17--22}, year={2006} } """ _DESCRIPTION = """\ TITML-IDN (Tokyo Institute of Technology Multilingual - Indonesian) is collected to build a pioneering Indonesian Large Vocabulary Continuous Speech Recognition (LVCSR) System. In order to build an LVCSR system, high accurate acoustic models and large-scale language models are essential. Since Indonesian speech corpus was not available yet, we tried to collect speech data from 20 Indonesian native speakers (11 males and 9 females) to construct a speech corpus for training the acoustic model based on Hidden Markov Models (HMMs). A text corpus which was collected by ILPS, Informatics Institute, University of Amsterdam, was used to build a 40K-vocabulary dictionary and a n-gram language model. """ _HOMEPAGE = "http://research.nii.ac.jp/src/en/TITML-IDN.html" _LICENSE = "For research purposes only. If you use this corpus, you have to cite (Lestari et al, 2006)." _URLs = {"titml-idn": "https://huggingface.co/datasets/holylovenia/TITML-IDN/resolve/main/IndoLVCSR.zip"} _SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION] _SOURCE_VERSION = "1.0.0" _NUSANTARA_VERSION = "1.0.0" class TitmlIdn(datasets.GeneratorBasedBuilder): """TITML-IDN is a speech recognition dataset containing Indonesian speech collected with transcriptions from newpaper and magazine articles.""" BUILDER_CONFIGS = [ NusantaraConfig( name="titml_idn_source", version=datasets.Version(_SOURCE_VERSION), description="TITML-IDN source schema", schema="source", subset_id="titml_idn", ), NusantaraConfig( name="titml_idn_nusantara_sptext", version=datasets.Version(_NUSANTARA_VERSION), description="TITML-IDN Nusantara schema", schema="nusantara_sptext", subset_id="titml_idn", ), ] DEFAULT_CONFIG_NAME = "titml_idn_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=16_000), "text": datasets.Value("string"), } ) elif self.config.schema == "nusantara_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 = dl_manager.download_and_extract(_URLs["titml-idn"]) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": base_path}, ), ] def _generate_examples(self, filepath: Path, n_speakers=20): if self.config.schema == "source" or self.config.schema == "nusantara_sptext": for speaker_id in range(1, n_speakers + 1): speaker_id = str(speaker_id).zfill(2) dir_path = os.path.join(filepath, speaker_id) transcription_path = os.path.join(dir_path, "script~") with open(transcription_path, "r+") as f: for line in f: audio_id = line[2:8] text = line[9:].strip() wav_path = os.path.join(dir_path, "{}.wav".format(audio_id)) if os.path.exists(wav_path): if self.config.schema == "source": ex = { "id": audio_id, "speaker_id": speaker_id, "path": wav_path, "audio": wav_path, "text": text, } yield audio_id, ex elif self.config.schema == "nusantara_sptext": ex = { "id": audio_id, "speaker_id": speaker_id, "path": wav_path, "audio": wav_path, "text": text, "metadata": { "speaker_age": None, "speaker_gender": None, } } yield audio_id, ex else: raise ValueError(f"Invalid config: {self.config.name}")