# coding=utf-8 # Copyright 2025 The Leo-Ai and HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from collections import OrderedDict import datasets logger = datasets.logging.get_logger(__name__) """ Soreva Dataset""" _SOREVA_LANG_TO_ID = OrderedDict([ ("Afrikaans", "af"), ("Bafia", "ksf"), ("Bafut", "bfd"), ("Baka", "bdh"), ("Bakoko", "bkh"), ("Bamun", "bax"), ("Basaa", "bas"), ("Duala", "dua"), ("Ejagham", "etu"), ("Eton", "eto"), ("Ewondo", "ewo"), ("Fe", "fmp"), ("Fulfulde", "fub"), ("Gbaya", "gya"), ("Ghamála", "bbj"), ("Hausa", "ha"), ("Igbo", "ibo"), ("isiXhosa", "xho"), ("isiZulu", "zul"), ("Isu", "isu"), ("Kera", "ker"), ("Kiswahili", "swa"), ("Kom", "bkm"), ("Kwasio", "kqs"), ("Lamso", "lns"), ("Lingala", "lin"), ("Maka", "mcp"), ("Malagasy", "mg"), ("Medumba", "byv"), ("Mka", "bqz"), ("Mundang", "mua"), ("Nda", "nda"), ("Ngiemboon", "nnh"), ("Ngombala", "nla"), ("Nomaande", "lem"), ("Nugunu", "yas"), ("Pidgin", "pcm"), ("Pulaar", "fuc"), ("Sepedi", "nso"), ("Tuki", "bag"), ("Tunen", "tvu"), ("Twi", "twi"), ("Vute", "vut"), ("Wolof", "wol"), ("Yambeta", "yat"), ("Yangben", "yav"), ("Yemba", "ybb"), ("Yoruba", "yor"), ("Éwé", "ewe") ]) _SOREVA_LANG_SHORT_TO_LONG = {v: k for k, v in _SOREVA_LANG_TO_ID.items()} _SOREVA_LANG = sorted([ "af_za", "bag_cm", "bas_cm", "bax_cm", "bbj_cm", "bqz_cm", "bdh_cm", "bfd_cm", "bkh_cm", "bkm_cm", "ksf_cm", "byv_cm", "dua_cm", "ewe_tg", "etu_cm", "eto_cm", "ewo_cm", "fmp_cm", "fub_cm", "fuc_sn", "gya_cf", "ha_ng", "ibo_ng", "isu_cm", "ker_td", "kqs_cm", "lem_cm", "lin_cd", "lns_cm", "mcp_cm", "mg_mg", "tvu_cm", "mua_cm", "nda_cm", "nla_cm", "nnh_cm", "nso_za", "pcm_cm", "swa_ke", "twi_gh", "vut_cm", "wol_sn", "xho_za", "yas_cm", "yav_cm", "ybb_cm", "yor_ng", "zul_za",'yat_cm' ]) _SOREVA_LONG_TO_LANG = {_SOREVA_LANG_SHORT_TO_LONG["_".join(k.split("_")[:-1]) or k]: k for k in _SOREVA_LANG} _SOREVA_LANG_TO_LONG = {v: k for k, v in _SOREVA_LONG_TO_LANG.items()} _ALL_LANG = _SOREVA_LANG _ALL_CONFIGS = [] for langs in _SOREVA_LANG: _ALL_CONFIGS.append(langs) _ALL_CONFIGS.append("all") # TODO(Soreva) _DESCRIPTION = "SOREVA is a multilingual speech dataset designed for the evaluation" \ "of text-to-speech (TTS) and speech representation models in low-resource African languages. " \ "This dataset specifically targets out-of-domain generalization, addressing the lack of evaluation sets for" \ " languages typically trained on narrow-domain corpora such as religious texts." _CITATION = "" _HOMEPAGE_URL = "" _BASE_PATH = "data/{langs}/" _DATA_URL = _BASE_PATH + "audio/{split}.tar.gz" _META_URL = _BASE_PATH + "{split}.tsv" class sorevaConfig(datasets.BuilderConfig): """BuilderConfig for xtreme-s""" def __init__( self, name, description, citation, homepage ): super(sorevaConfig, self).__init__( name=self.name, version=datasets.Version("1.0.0", ""), description=self.description, ) self.name = name self.description = description self.citation = citation self.homepage = homepage def _build_config(name): return sorevaConfig( name=name, description=_DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE_URL, ) class soreva(datasets.GeneratorBasedBuilder): DEFAULT_WRITER_BATCH_SIZE = 1000 BUILDER_CONFIGS = [_build_config(name) for name in _ALL_CONFIGS] def _info(self): langs = _ALL_CONFIGS features = datasets.Features( { "path": datasets.Value("string"), "audio": datasets.Audio(sampling_rate=16_000), "transcription": datasets.Value("string"), "raw_transcription": datasets.Value("string"), "gender": datasets.ClassLabel(names=["male", "female", "other"]), "lang_id": datasets.ClassLabel(names=langs), "language": datasets.Value("string"), } ) return datasets.DatasetInfo( description=self.config.description + "\n" + _DESCRIPTION, features=features, supervised_keys=("audio", "transcription"), homepage=self.config.homepage, citation=self.config.citation + "\n" + _CITATION, ) # soreva def _split_generators(self, dl_manager): all_splits = ["train", "dev", "test"] available_splits = [] if self.config.name == "all": langs = _SOREVA_LANG else: langs = [self.config.name] data_urls = {} meta_urls = {} for split in all_splits: try: if self.config.name == "all": data_urls[split] = [_DATA_URL.format(langs=lang, split=split) for lang in langs] meta_urls[split] = [_META_URL.format(langs=lang, split=split) for lang in langs] else: data_urls[split] = [_DATA_URL.format(langs=self.config.name, split=split)] meta_urls[split] = [_META_URL.format(langs=self.config.name, split=split)] # Test of downloading existing split dl_manager.download(meta_urls[split]) available_splits.append(split) except Exception as e: logger.warning(f"Split '{split}' not available : {e}") archive_paths = dl_manager.download({s: data_urls[s] for s in available_splits}) local_extracted_archives = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {} archive_iters = {s: [dl_manager.iter_archive(p) for p in archive_paths[s]] for s in available_splits} meta_paths = dl_manager.download({s: meta_urls[s] for s in available_splits}) split_gens = [] for split in available_splits: split_name = { "train": datasets.Split.TRAIN, "dev": datasets.Split.VALIDATION, "test": datasets.Split.TEST }[split] split_gens.append( datasets.SplitGenerator( name=split_name, gen_kwargs={ "local_extracted_archives": local_extracted_archives.get(split, [None] * len(meta_paths.get(split))), "archive_iters": archive_iters.get(split), "text_paths": meta_paths.get(split) }, ) ) return split_gens def _get_data(self, lines, lang_id): data = {} gender_to_id = {"MALE": 0, "FEMALE": 1, "OTHER": 2} for line in lines: if isinstance(line, bytes): line = line.decode("utf-8") ( file_name, raw_transcription, transcription, gender, ) = line.strip().split("\t") data[file_name] = { "raw_transcription": raw_transcription, "transcription": transcription, "gender": gender_to_id[gender], "lang_id": _SOREVA_LANG.index(lang_id), "language": _SOREVA_LANG_TO_LONG[lang_id], } return data def _generate_examples(self, local_extracted_archives, archive_iters, text_paths): assert len(local_extracted_archives) == len(archive_iters) == len(text_paths) key = 0 if self.config.name == "all": langs = _SOREVA_LANG else: langs = [self.config.name] for archive, text_path, local_extracted_path, lang_id in zip(archive_iters, text_paths, local_extracted_archives, langs): with open(text_path, encoding="utf-8") as f: lines = f.readlines() data = self._get_data(lines, lang_id) for audio_path, audio_file in archive: audio_filename = audio_path.split("/")[-1] if audio_filename not in data.keys(): continue result = data[audio_filename] extracted_audio_path = ( os.path.join(local_extracted_path, audio_filename) if local_extracted_path is not None else None ) result["path"] = extracted_audio_path result["audio"] = {"path": audio_path, "bytes": audio_file.read()} yield key, result key += 1