# 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 = "2024.06.20" 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