Datasets:
Languages:
Sundanese
Tags:
text-to-speech
File size: 7,652 Bytes
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import csv
import os
from pathlib import Path
from typing import List
import datasets
from nusacrowd.utils import schemas
from nusacrowd.utils.configs import NusantaraConfig
from nusacrowd.utils.constants import (DEFAULT_NUSANTARA_VIEW_NAME,
DEFAULT_SOURCE_VIEW_NAME, Tasks)
_DATASETNAME = "su_id_tts"
_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_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 = """\
This data set contains high-quality transcribed audio data for Sundanese. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number.
The data set has been manually quality checked, but there might still be errors.
This dataset was collected by Google in collaboration with Universitas Pendidikan Indonesia.
"""
_HOMEPAGE = "http://openslr.org/44/"
_LICENSE = "CC BY-SA 4.0"
_URLs = {
_DATASETNAME: {
"female": "https://www.openslr.org/resources/44/su_id_female.zip",
"male": "https://www.openslr.org/resources/44/su_id_male.zip",
}
}
_SUPPORTED_TASKS = [Tasks.TEXT_TO_SPEECH]
_SOURCE_VERSION = "1.0.0"
_NUSANTARA_VERSION = "1.0.0"
class SuIdTTS(datasets.GeneratorBasedBuilder):
"""su_id_tts contains high-quality Multi-speaker TTS data for Sundanese (SU-ID)."""
BUILDER_CONFIGS = [
NusantaraConfig(
name="su_id_tts_source",
version=datasets.Version(_SOURCE_VERSION),
description="SU_ID_TTS source schema",
schema="source",
subset_id="su_id_tts",
),
NusantaraConfig(
name="su_id_tts_nusantara_sptext",
version=datasets.Version(_NUSANTARA_VERSION),
description="SU_ID_TTS Nusantara schema",
schema="nusantara_sptext",
subset_id="su_id_tts",
),
]
DEFAULT_CONFIG_NAME = "su_id_tts_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"),
"gender": 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]:
male_path = Path(dl_manager.download_and_extract(_URLs[_DATASETNAME]["male"]))
female_path = Path(dl_manager.download_and_extract(_URLs[_DATASETNAME]["female"]))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"male_filepath": male_path,
"female_filepath": female_path,
},
),
]
def _generate_examples(self, male_filepath: Path, female_filepath: Path):
if self.config.schema == "source" or self.config.schema == "nusantara_sptext":
tsv_m = os.path.join(male_filepath, "su_id_male", "line_index.tsv")
tsv_f = os.path.join(female_filepath, "su_id_female", "line_index.tsv")
with open(tsv_m, "r") as file:
tsv_m_data = csv.reader(file, delimiter="\t")
for line in tsv_m_data:
spk_trans_info = line[0].split("_")
if self.config.schema == "source":
ex = {
"id": line[0],
"speaker_id": spk_trans_info[0] + "_" + spk_trans_info[1],
"path": os.path.join(male_filepath, "su_id_male", "wavs", "{}.wav".format(line[0])),
"audio": os.path.join(male_filepath, "su_id_male", "wavs", "{}.wav".format(line[0])),
"text": line[2],
"gender": spk_trans_info[0][2],
}
yield line[0], ex
elif self.config.schema == "nusantara_sptext":
ex = {
"id": line[0],
"speaker_id": spk_trans_info[0] + "_" + spk_trans_info[1],
"path": os.path.join(male_filepath, "su_id_male", "wavs", "{}.wav".format(line[0])),
"audio": os.path.join(male_filepath, "su_id_male", "wavs", "{}.wav".format(line[0])),
"text": line[2],
"metadata": {
"speaker_age": None,
"speaker_gender": spk_trans_info[0][2],
},
}
yield line[0], ex
with open(tsv_f, "r") as file:
tsv_f_data = csv.reader(file, delimiter="\t")
for line in tsv_f_data:
spk_trans_info = line[0].split("_")
if self.config.schema == "source":
ex = {
"id": line[0],
"speaker_id": spk_trans_info[0] + "_" + spk_trans_info[1],
"path": os.path.join(female_filepath, "su_id_female", "wavs", "{}.wav".format(line[0])),
"audio": os.path.join(female_filepath, "su_id_female", "wavs", "{}.wav".format(line[0])),
"text": line[2],
"gender": spk_trans_info[0][2],
}
yield line[0], ex
elif self.config.schema == "nusantara_sptext":
ex = {
"id": line[0],
"speaker_id": spk_trans_info[0] + "_" + spk_trans_info[1],
"path": os.path.join(female_filepath, "su_id_female", "wavs", "{}.wav".format(line[0])),
"audio": os.path.join(female_filepath, "su_id_female", "wavs", "{}.wav".format(line[0])),
"text": line[2],
"metadata": {
"speaker_age": None,
"speaker_gender": spk_trans_info[0][2],
},
}
yield line[0], ex
else:
raise ValueError(f"Invalid config: {self.config.name}")
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