Datasets:

SEACrowd
/

su_id_tts

Languages:

Sundanese

Tags:

text-to-speech

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su_id_tts / su_id_tts.py

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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}")