diff --git a/seacrowd/sea_datasets/asr_smaldusc/__init__.py b/seacrowd/sea_datasets/asr_smaldusc/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/seacrowd/sea_datasets/asr_smaldusc/asr_smaldusc.py b/seacrowd/sea_datasets/asr_smaldusc/asr_smaldusc.py new file mode 100644 index 000000000..007c296de --- /dev/null +++ b/seacrowd/sea_datasets/asr_smaldusc/asr_smaldusc.py @@ -0,0 +1,182 @@ +# coding=utf-8 +# Copyright 2022 The 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 pathlib import Path +from typing import Dict, List, Tuple + +import datasets + +from seacrowd.utils import schemas +from seacrowd.utils.configs import SEACrowdConfig +from seacrowd.utils.constants import Licenses, Tasks + +# no bibtex citation +_CITATION = "" +_DATASETNAME = "asr_smaldusc" +_DESCRIPTION = """\ +This open-source dataset consists of 4.8 hours of transcribed Malay scripted +speech focusing on daily use sentences, where 2,839 utterances contributed by +ten speakers were contained. +""" + +_HOMEPAGE = "https://magichub.com/datasets/malay-scripted-speech-corpus-daily-use-sentence/" +_LANGUAGES = ["zlm"] +_LICENSE = Licenses.CC_BY_NC_ND_4_0.value +_LOCAL = False +_URLS = { + _DATASETNAME: "https://magichub.com/df/df.php?file_name=Malay_Scripted_Speech_Corpus_Daily_Use_Sentence.zip", +} +_SUPPORTED_TASKS = [Tasks.TEXT_TO_SPEECH, Tasks.SPEECH_RECOGNITION] + +_SOURCE_VERSION = "1.0.0" +_SEACROWD_VERSION = "1.0.0" + + +class ASRSmaldusc(datasets.GeneratorBasedBuilder): + """ASR-Smaldusc consists transcribed Malay scripted speech focusing on daily use sentences.""" + + SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) + SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) + + SEACROWD_SCHEMA_NAME = "sptext" + + BUILDER_CONFIGS = [ + SEACrowdConfig( + name=f"{_DATASETNAME}_source", + version=SOURCE_VERSION, + description=f"{_DATASETNAME} source schema", + schema="source", + subset_id=_DATASETNAME, + ), + SEACrowdConfig( + name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}", + version=SEACROWD_VERSION, + description=f"{_DATASETNAME} SEACrowd schema", + schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}", + subset_id=_DATASETNAME, + ), + ] + + DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" + + def _info(self) -> datasets.DatasetInfo: + + if self.config.schema == "source": + features = datasets.Features( + { + "id": datasets.Value("string"), + "channel": datasets.Value("string"), + "uttrans_id": datasets.Value("string"), + "speaker_id": datasets.Value("string"), + "prompt": datasets.Value("string"), + "transcription": datasets.Value("string"), + "path": datasets.Value("string"), + "audio": datasets.Audio(sampling_rate=16_000), + "speaker_gender": datasets.Value("string"), + "speaker_age": datasets.Value("int64"), + "speaker_region": datasets.Value("string"), + "speaker_device": datasets.Value("string"), + } + ) + + elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": + features = schemas.speech_text_features + + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION, + ) + + def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: + """Returns SplitGenerators.""" + + data_paths = { + _DATASETNAME: Path(dl_manager.download_and_extract(_URLS[_DATASETNAME])), + } + + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepath": data_paths[_DATASETNAME], + "split": "train", + }, + ) + ] + + def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: + """Yields examples as (key, example) tuples.""" + + # read UTTRANSINFO file + # columns: channel, uttrans_id, speaker_id, prompt, transcription + uttransinfo_filepath = os.path.join(filepath, "UTTRANSINFO.txt") + with open(uttransinfo_filepath, "r", encoding="utf-8") as uttransinfo_file: + uttransinfo_data = uttransinfo_file.readlines() + uttransinfo_data = uttransinfo_data[1:] # remove header + uttransinfo_data = [s.strip("\n").split("\t") for s in uttransinfo_data] + + # read SPKINFO file + # columns: channel, speaker_id, gender, age, region, device + spkinfo_filepath = os.path.join(filepath, "SPKINFO.txt") + with open(spkinfo_filepath, "r", encoding="utf-8") as spkinfo_file: + spkinfo_data = spkinfo_file.readlines() + spkinfo_data = spkinfo_data[1:] # remove header + spkinfo_data = [s.strip("\n").split("\t") for s in spkinfo_data] + for i, s in enumerate(spkinfo_data): + if s[2] == "M": + s[2] = "male" + elif s[2] == "F": + s[2] = "female" + else: + s[2] = None + # dictionary of metadata of each speaker + spkinfo_dict = {s[1]: {"speaker_gender": s[2], "speaker_age": int(s[3]), "speaker_region": s[4], "speaker_device": s[5]} for s in spkinfo_data} + + num_sample = len(uttransinfo_data) + + for i in range(num_sample): + wav_path = os.path.join(filepath, "WAV", uttransinfo_data[i][2], uttransinfo_data[i][1]) + + if self.config.schema == "source": + example = { + "id": str(i), + "channel": uttransinfo_data[i][0], + "uttrans_id": uttransinfo_data[i][1], + "speaker_id": uttransinfo_data[i][2], + "prompt": uttransinfo_data[i][3], + "transcription": uttransinfo_data[i][4], + "path": wav_path, + "audio": wav_path, + "speaker_gender": spkinfo_dict[uttransinfo_data[i][2]]["speaker_gender"], + "speaker_age": spkinfo_dict[uttransinfo_data[i][2]]["speaker_age"], + "speaker_region": spkinfo_dict[uttransinfo_data[i][2]]["speaker_region"], + "speaker_device": spkinfo_dict[uttransinfo_data[i][2]]["speaker_device"], + } + elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": + example = { + "id": str(i), + "speaker_id": uttransinfo_data[i][2], + "path": wav_path, + "audio": wav_path, + "text": uttransinfo_data[i][4], + "metadata": {"speaker_age": spkinfo_dict[uttransinfo_data[i][2]]["speaker_age"], "speaker_gender": spkinfo_dict[uttransinfo_data[i][2]]["speaker_gender"]}, + } + + yield i, example