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import csv
import os
from typing import Dict, List
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import (DEFAULT_SEACROWD_VIEW_NAME,
DEFAULT_SOURCE_VIEW_NAME, Tasks)
_DATASETNAME = "su_id_asr"
_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
_LANGUAGES = ["sun"]
_LOCAL = False
_CITATION = """\
@inproceedings{sodimana18_sltu,
author={Keshan Sodimana and Pasindu {De Silva} and Supheakmungkol Sarin and Oddur Kjartansson and Martin Jansche and Knot Pipatsrisawat and Linne Ha},
title={{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Frameworks for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}},
year=2018,
booktitle={Proc. 6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018)},
pages={66--70},
doi={10.21437/SLTU.2018-14}
}
"""
_DESCRIPTION = """\
Sundanese ASR training data set containing ~220K utterances.
This dataset was collected by Google in Indonesia.
"""
_HOMEPAGE = "https://indonlp.github.io/nusa-catalogue/card.html?su_id_asr"
_LICENSE = "Attribution-ShareAlike 4.0 International."
_URLs = {
"su_id_asr": "https://www.openslr.org/resources/36/asr_sundanese_{}.zip",
}
_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class SuIdASR(datasets.GeneratorBasedBuilder):
"""su_id contains ~220K utterances for Sundanese ASR training data."""
BUILDER_CONFIGS = [
SEACrowdConfig(
name="su_id_asr_source",
version=datasets.Version(_SOURCE_VERSION),
description="SU_ID_ASR source schema",
schema="source",
subset_id="su_id_asr",
),
SEACrowdConfig(
name="su_id_asr_seacrowd_sptext",
version=datasets.Version(_SEACROWD_VERSION),
description="SU_ID_ASR Nusantara schema",
schema="seacrowd_sptext",
subset_id="su_id_asr",
),
]
DEFAULT_CONFIG_NAME = "su_id_asr_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 == "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 = {}
for id in range(10):
base_path[id] = dl_manager.download_and_extract(_URLs["su_id_asr"].format(str(id)))
for id in ["a", "b", "c", "d", "e", "f"]:
base_path[id] = dl_manager.download_and_extract(_URLs["su_id_asr"].format(str(id)))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": base_path},
),
]
def _generate_examples(self, filepath: Dict):
if self.config.schema == "source" or self.config.schema == "seacrowd_sptext":
for key, each_filepath in filepath.items():
tsv_file = os.path.join(each_filepath, "asr_sundanese", "utt_spk_text.tsv")
with open(tsv_file, "r") as file:
tsv_file = csv.reader(file, delimiter="\t")
for line in tsv_file:
audio_id, speaker_id, transcription_text = line[0], line[1], line[2]
wav_path = os.path.join(each_filepath, "asr_sundanese", "data", "{}".format(audio_id[:2]), "{}.flac".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": transcription_text,
}
yield audio_id, ex
elif self.config.schema == "seacrowd_sptext":
ex = {
"id": audio_id,
"speaker_id": speaker_id,
"path": wav_path,
"audio": wav_path,
"text": transcription_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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