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import os |
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import re |
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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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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import Licenses, Tasks |
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_CITATION = """\ |
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@inproceedings{kjartansson18_sltu, |
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author={Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha}, |
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title={{Crowd-Sourced Speech Corpora for Javanese, Sundanese, Sinhala, Nepali, and Bangladeshi Bengali}}, |
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year=2018, |
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booktitle={Proc. 6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018)}, |
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pages={52--55}, |
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doi={10.21437/SLTU.2018-11} |
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} |
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""" |
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_DATASETNAME = "openslr" |
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_DESCRIPTION = """\ |
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This data set contains transcribed high-quality audio of Javanese, Sundanese, Burmese, Khmer. This data set\ |
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come from 3 different projects under OpenSLR initiative |
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""" |
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_HOMEPAGE = "https://www.openslr.org/resources.php" |
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_LANGUAGES = ["mya", "jav", "sun", "khm"] |
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_LICENSE = Licenses.CC_BY_SA_4_0.value |
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_LOCAL = False |
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_RESOURCES = { |
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"SLR35": { |
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"language": "jav", |
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"files": [ |
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"asr_javanese_0.zip", |
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"asr_javanese_1.zip", |
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"asr_javanese_2.zip", |
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"asr_javanese_3.zip", |
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"asr_javanese_4.zip", |
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"asr_javanese_5.zip", |
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"asr_javanese_6.zip", |
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"asr_javanese_7.zip", |
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"asr_javanese_8.zip", |
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"asr_javanese_9.zip", |
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"asr_javanese_a.zip", |
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"asr_javanese_b.zip", |
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"asr_javanese_c.zip", |
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"asr_javanese_d.zip", |
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"asr_javanese_e.zip", |
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"asr_javanese_f.zip", |
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], |
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"index_files": ["asr_javanese/utt_spk_text.tsv"] * 16, |
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"data_dirs": ["asr_javanese/data"] * 16, |
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}, |
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"SLR36": { |
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"language": "sun", |
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"files": [ |
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"asr_sundanese_0.zip", |
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"asr_sundanese_1.zip", |
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"asr_sundanese_2.zip", |
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"asr_sundanese_3.zip", |
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"asr_sundanese_4.zip", |
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"asr_sundanese_5.zip", |
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"asr_sundanese_6.zip", |
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"asr_sundanese_7.zip", |
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"asr_sundanese_8.zip", |
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"asr_sundanese_9.zip", |
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"asr_sundanese_a.zip", |
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"asr_sundanese_b.zip", |
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"asr_sundanese_c.zip", |
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"asr_sundanese_d.zip", |
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"asr_sundanese_e.zip", |
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"asr_sundanese_f.zip", |
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], |
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"index_files": ["asr_sundanese/utt_spk_text.tsv"] * 16, |
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"data_dirs": ["asr_sundanese/data"] * 16, |
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}, |
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"SLR41": { |
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"language": "jav", |
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"files": ["jv_id_female.zip", "jv_id_male.zip"], |
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"index_files": ["jv_id_female/line_index.tsv", "jv_id_male/line_index.tsv"], |
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"data_dirs": ["jv_id_female/wavs", "jv_id_male/wavs"], |
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}, |
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"SLR42": { |
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"language": "khm", |
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"files": ["km_kh_male.zip"], |
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"index_files": ["km_kh_male/line_index.tsv"], |
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"data_dirs": ["km_kh_male/wavs"], |
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}, |
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"SLR44": { |
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"language": "sun", |
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"files": ["su_id_female.zip", "su_id_male.zip"], |
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"index_files": ["su_id_female/line_index.tsv", "su_id_male/line_index.tsv"], |
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"data_dirs": ["su_id_female/wavs", "su_id_male/wavs"], |
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}, |
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"SLR80": { |
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"language": "mya", |
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"files": ["my_mm_female.zip"], |
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"index_files": ["line_index.tsv"], |
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"data_dirs": [""], |
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}, |
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} |
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_URLS = {_DATASETNAME: "https://openslr.org/resources/{subset}"} |
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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 OpenSLRDataset(datasets.GeneratorBasedBuilder): |
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"""This data set contains transcribed high-quality audio of Javanese, Sundanese, Burmese, Khmer. This data set |
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come from 3 different projects under OpenSLR initiative""" |
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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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SEACrowdConfig(name=f"{_DATASETNAME}_{subset}_{_RESOURCES[subset]['language']}_source", version=datasets.Version(_SOURCE_VERSION), description=f"{_DATASETNAME} source schema", schema="source", subset_id=f"{_DATASETNAME}") |
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for subset in _RESOURCES.keys() |
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] + [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_{subset}_{_RESOURCES[subset]['language']}_seacrowd_sptext", version=datasets.Version(_SEACROWD_VERSION), description=f"{_DATASETNAME} SEACrowd schema", schema="seacrowd_sptext", subset_id=f"{_DATASETNAME}" |
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) |
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for subset in _RESOURCES.keys() |
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] |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_SLR41_jav_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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"path": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=48_000), |
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"sentence": 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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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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"""Returns SplitGenerators.""" |
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subset = self.config.name.split("_")[1] |
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urls = [f"{_URLS[_DATASETNAME].format(subset=subset[3:])}/{file}" for file in _RESOURCES[subset]["files"]] |
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data_dir = dl_manager.download_and_extract(urls) |
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path_to_indexs = [os.path.join(path, f"{_RESOURCES[subset]['index_files'][i]}") for i, path in enumerate(data_dir)] |
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path_to_datas = [os.path.join(path, f"{_RESOURCES[subset]['data_dirs'][i]}") for i, path in enumerate(data_dir)] |
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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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"filepath": [path_to_indexs, path_to_datas], |
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"split": "train", |
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}, |
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) |
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] |
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def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]: |
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"""Yields examples as (key, example) tuples.""" |
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subset = self.config.name.split("_")[1] |
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path_to_indexs, path_to_datas = filepath[0], filepath[1] |
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counter = -1 |
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if subset in ["SLR35", "SLR36"]: |
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sentence_index = {} |
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for i, path_to_index in enumerate(path_to_indexs): |
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with open(path_to_index, encoding="utf-8") as f: |
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lines = f.readlines() |
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for id_, line in enumerate(lines): |
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field_values = re.split(r"\t\t?", line.strip()) |
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filename, user_id, sentence = field_values |
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sentence_index[filename] = sentence |
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for path_to_data in sorted(Path(path_to_datas[i]).rglob("*.flac")): |
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filename = path_to_data.stem |
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if path_to_data.stem not in sentence_index: |
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continue |
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path = str(path_to_data.resolve()) |
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sentence = sentence_index[filename] |
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counter += 1 |
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if self.config.schema == "source": |
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example = {"path": path, "audio": path, "sentence": sentence} |
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elif self.config.schema == "seacrowd_sptext": |
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example = { |
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"id": counter, |
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"path": path, |
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"audio": path, |
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"text": sentence, |
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"speaker_id": user_id, |
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"metadata": { |
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"speaker_age": None, |
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"speaker_gender": None, |
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}, |
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} |
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yield counter, example |
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else: |
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for i, path_to_index in enumerate(path_to_indexs): |
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geneder = "female" if "female" in path_to_index else "male" |
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with open(path_to_index, encoding="utf-8") as f: |
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lines = f.readlines() |
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for id_, line in enumerate(lines): |
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line = re.sub(r"\t[^\t]*\t", "\t", line.strip()) |
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field_values = re.split(r"\t\t?", line) |
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if len(field_values) != 2: |
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continue |
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filename, sentence = field_values |
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path = os.path.join(path_to_datas[i], f"{filename}.wav") |
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counter += 1 |
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if self.config.schema == "source": |
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example = {"path": path, "audio": path, "sentence": sentence} |
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elif self.config.schema == "seacrowd_sptext": |
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example = { |
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"id": counter, |
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"path": path, |
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"audio": path, |
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"text": sentence, |
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"speaker_id": None, |
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"metadata": { |
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"speaker_age": None, |
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"speaker_gender": geneder, |
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}, |
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} |
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yield counter, example |
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