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from pathlib import Path |
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from typing import Dict, List, Tuple |
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import datasets |
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import json |
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import os |
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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import Tasks |
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from zipfile import ZipFile |
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_CITATION = """\ |
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@inproceedings{sani-cocosda-2012, |
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title = "Towards Language Preservation: Preliminary Collection and Vowel Analysis of {I}ndonesian Ethnic Speech Data", |
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author = "Sani, Auliya and Sakti, Sakriani and Neubig, Graham and Toda, Tomoki and Mulyanto, Adi and Nakamura, Satoshi", |
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booktitle = "Proc. Oriental COCOSDA", |
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year = "2012", |
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pages = "118--122" |
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address = "Macau, China" |
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} |
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""" |
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_LOCAL = False |
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_LANGUAGES = ["sun", "jav"] |
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_DATASETNAME = "indspeech_news_ethnicsr" |
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_DESCRIPTION = """ |
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INDspeech_NEWS_EthnicSR is a collection of Indonesian ethnic speech corpora for Javanese and Sundanese for Indonesian ethnic speech recognition. It was developed in 2012 by the Nara Institute of Science and Technology (NAIST, Japan) in collaboration with the Bandung Institute of Technology (ITB, Indonesia) [Sani et al., 2012]. |
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""" |
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_HOMEPAGE = "https://github.com/s-sakti/data_indsp_news_ethnicsr" |
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_LICENSE = "CC-BY-NC-SA 4.0" |
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_URLS = { |
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_DATASETNAME: "https://github.com/s-sakti/data_indsp_news_ethnicsr/archive/refs/heads/main.zip", |
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} |
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_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class IndSpeechNewsEthnicSR(datasets.GeneratorBasedBuilder): |
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"""INDspeech_NEWS_EthnicSR is a collection of Indonesian ethnic speech corpora for Javanese and Sundanese for Indonesian ethnic speech recognition. It was developed in 2012 by the Nara Institute of Science and Technology (NAIST, Japan) in collaboration with the Bandung Institute of Technology (ITB, Indonesia) [Sani et al., 2012].""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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BUILDER_CONFIGS = [] |
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for fold_id in ["overlap", "nooverlap"]: |
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for fold_name in ['jv', "su"]: |
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BUILDER_CONFIGS.extend( |
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[SEACrowdConfig( |
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name=f"indspeech_news_ethnicsr_{fold_name}_{fold_id}_source", |
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version=_SOURCE_VERSION, |
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description="indspeech_news_ethnicsr source schema", |
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schema="source", |
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subset_id=f"indspeech_news_ethnicsr_{fold_name}_{fold_id}" |
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), |
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SEACrowdConfig( |
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name=f"indspeech_news_ethnicsr_{fold_name}_{fold_id}_seacrowd_sptext", |
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version=_SOURCE_VERSION, |
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description="indspeech_news_ethnicsr Nusantara schema", |
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schema="seacrowd_sptext", |
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subset_id=f"indspeech_news_ethnicsr_{fold_name}_{fold_id}" |
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),] |
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) |
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DEFAULT_CONFIG_NAME = "indspeech_news_ethnicsr_jv_nooverlap_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"speaker_id": datasets.Value("string"), |
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"path": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=16_000), |
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"text": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == "seacrowd_sptext": |
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features = schemas.speech_text_features |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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task_templates=[datasets.AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")], |
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) |
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def _get_fold_name_id(self): |
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subset_id = self.config.subset_id |
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subset_id_list = subset_id.split('_') |
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fold_name = subset_id_list[-2] |
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fold_id = subset_id_list[-1] |
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if fold_id == "overlap": |
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fold_id = 1 |
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elif fold_id == "nooverlap": |
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fold_id = 2 |
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return fold_name, fold_id |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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fold_name, fold_id = self._get_fold_name_id() |
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if fold_name == 'su': |
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fold_name1 = "Sunda" |
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fold_name2 = 'Snd' |
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else: |
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fold_name1 = 'Jawa' |
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fold_name2 = 'Jaw' |
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urls = _URLS[_DATASETNAME] |
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data_dir = Path(dl_manager.download_and_extract(urls)) |
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text_file = os.path.join(data_dir, f"data_indsp_news_ethnicsr-main/{fold_name1}/text/transcript.txt") |
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wav_folder = os.path.join(data_dir, f"data_indsp_news_ethnicsr-main/{fold_name1}/speech/16kHz/") |
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train_list = os.path.join(data_dir, f"data_indsp_news_ethnicsr-main/{fold_name1}/lst/dataset{fold_id}_train_news_{fold_name2}.lst") |
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test_list = os.path.join(data_dir, f"data_indsp_news_ethnicsr-main/{fold_name1}/lst/dataset{fold_id}_test_news_{fold_name2}.lst") |
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for speaker_id in range(1, 11): |
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speaker_id = "%03d" % (speaker_id) |
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zip_file = os.path.join(wav_folder, f"{fold_name2}{speaker_id}.zip") |
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out_folder = os.path.join(wav_folder, f"{fold_name2}{speaker_id}") |
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if not os.path.exists(out_folder): |
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with ZipFile(zip_file, 'r') as f: |
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f.extractall(out_folder) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"wav_folder": wav_folder, |
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"text_path": text_file, |
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"split": "train", |
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"fold_name": fold_name, |
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"file_list": train_list, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"wav_folder": wav_folder, |
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"text_path": text_file, |
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"split": "test", |
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"fold_name": fold_name, |
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"file_list": test_list, |
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}, |
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) |
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] |
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def _generate_examples(self, wav_folder: Path, text_path: Path, split: str, fold_name: str, file_list: Path) -> Tuple[int, Dict]: |
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if fold_name == 'su': |
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fold_name2 = 'Snd' |
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else: |
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fold_name2 = 'Jaw' |
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id2text = {} |
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with open(text_path, "r", encoding='unicode_escape') as f: |
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for text_idx, line in enumerate(f.readlines()): |
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id2text.update({"%04d" % (text_idx + 1):line.strip()}) |
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wave_list = [] |
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with open(file_list) as f: |
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for l in f.readlines(): |
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audio_id = l.strip()[:-4] |
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speaker_id = audio_id.split('_')[0][-3:] |
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text_id = audio_id.split('_')[-1] |
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text = id2text[text_id] |
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wav_path = os.path.join(wav_folder, audio_id.split('_')[0], l.strip()) |
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if not os.path.exists(wav_path): |
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print('no exisit wav_path', wav_path) |
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assert os.path.exists(wav_path) |
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if self.config.schema == "source": |
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ex = { |
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"id": audio_id, |
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"speaker_id": speaker_id, |
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"path": wav_path, |
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"audio": wav_path, |
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"text": text, |
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} |
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yield audio_id, ex |
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elif self.config.schema == "seacrowd_sptext": |
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ex = { |
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"id": audio_id, |
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"speaker_id": speaker_id, |
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"path": wav_path, |
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"audio": wav_path, |
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"text": text, |
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"metadata": { |
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"speaker_age": None, |
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"speaker_gender": audio_id.split("_")[1], |
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} |
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} |
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yield audio_id, ex |
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