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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}")
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