import csv import os from pathlib import Path from typing import 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_tts" _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 = """\ 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" _SEACROWD_VERSION = "2024.06.20" class SuIdTTS(datasets.GeneratorBasedBuilder): """su_id_tts contains high-quality Multi-speaker TTS data for Sundanese (SU-ID).""" BUILDER_CONFIGS = [ SEACrowdConfig( name="su_id_tts_source", version=datasets.Version(_SOURCE_VERSION), description="SU_ID_TTS source schema", schema="source", subset_id="su_id_tts", ), SEACrowdConfig( name="su_id_tts_seacrowd_sptext", version=datasets.Version(_SEACROWD_VERSION), description="SU_ID_TTS Nusantara schema", schema="seacrowd_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 == "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]: 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 == "seacrowd_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 == "seacrowd_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 == "seacrowd_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}")