librivox_indonesia.py

# 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# 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.
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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 Tasks, DEFAULT_SOURCE_VIEW_NAME, DEFAULT_SEACROWD_VIEW_NAME

import pandas as pd

_CITATION = """\
@misc{
   research, 
   title={indonesian-nlp/librivox-indonesia · datasets at hugging face}, 
   url={https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia},
   author={Indonesian-nlp}
} 
"""

_DATASETNAME = "librivox_indonesia"
_DESCRIPTION = """\
The LibriVox Indonesia dataset consists of MP3 audio and a corresponding text file we generated from the public domain audiobooks LibriVox. 
We collected only languages in Indonesia for this dataset. 
The original LibriVox audiobooks or sound files' duration varies from a few minutes to a few hours. 
Each audio file in the speech dataset now lasts from a few seconds to a maximum of 20 seconds.
We converted the audiobooks to speech datasets using the forced alignment software we developed. 
It supports multilingual, including low-resource languages, such as Acehnese, Balinese, or Minangkabau. 
We can also use it for other languages without additional work to train the model.
The dataset currently consists of 8 hours in 7 languages from Indonesia. 
We will add more languages or audio files as we collect them. 
"""

_HOMEPAGE = "https://huggingface.co/indonesian-nlp/librivox-indonesia"

_LICENSE = "CC0"

_URLS = {
    _DATASETNAME: "https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia/resolve/main/data",
}
_LANGUAGES = {"ind", "sun", "jav", "min", "bug", "ban", "ace"}
_LANG_CODE = {
    "ind": ["ind", "indonesian"],
    "sun": ["sun", "sundanese"],
    "jav": ["jav", "javanese"],
    "min": ["min", "minangkabau"],
    "bug": ["bug", "bugisnese"],
    "ban": ["bal", "balinese"],
    "ace": ["ace", "acehnese"]
}
_LOCAL = False
_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]  # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]

_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"


class LibrivoxIndonesia(datasets.GeneratorBasedBuilder):
    """
    Librivox-indonesia is a speech-to-text dataset in 7 languages available in Indonesia.
    The default dataloader contains all languages, while the other available dataloaders contain a designated language.
    """

    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)

    BUILDER_CONFIGS = [
        SEACrowdConfig(
            name="librivox_indonesia_source",
            version=_SOURCE_VERSION,
            description="Librivox-Indonesia source schema for all languages",
            schema="source",
            subset_id="librivox_indonesia",
        )] + [
        SEACrowdConfig(
            name="librivox_indonesia_{lang}_source".format(lang=lang),
            version=_SOURCE_VERSION,
            description="Librivox-Indonesia source schema for {lang} languages".format(lang=_LANG_CODE[lang][1]),
            schema="source",
            subset_id="librivox_indonesia_{lang}".format(lang=lang),
        ) for lang in _LANGUAGES] + [
        SEACrowdConfig(
            name="librivox_indonesia_seacrowd_sptext",
            version=_SEACROWD_VERSION,
            description="Librivox-Indonesia Nusantara schema for all languages",
            schema="seacrowd_sptext",
            subset_id="librivox_indonesia",
        )] + [
        SEACrowdConfig(
            name="librivox_indonesia_{lang}_seacrowd_sptext".format(lang=lang),
            version=_SEACROWD_VERSION,
            description="Librivox-Indonesia Nusantara schema for {lang} languages".format(lang=_LANG_CODE[lang][1]),
            schema="seacrowd_sptext",
            subset_id="librivox_indonesia_{lang}".format(lang=lang),
        )for lang in _LANGUAGES]

    DEFAULT_CONFIG_NAME = "librivox_indonesia_source"

    def _info(self) -> datasets.DatasetInfo:
        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "path": datasets.Value("string"),
                    "language": datasets.Value("string"),
                    "reader": datasets.Value("string"),
                    "sentence": datasets.Value("string"),
                    "audio": datasets.features.Audio(sampling_rate=44100)
                }
            )
        elif self.config.schema == "seacrowd_sptext":
            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]:
        urls = _URLS[_DATASETNAME]

        audio_path = {}
        local_extracted_archive = {}
        metadata_path = {}
        splits = ["train", "test"]
        for split in splits:
            audio_path[split] = dl_manager.download(os.path.join(urls, "audio_{split}.tgz".format(split=split)))
            local_extracted_archive[split] = dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None
            metadata_path[split] = dl_manager.download_and_extract(
                os.path.join(urls, "metadata_{split}.csv.gz".format(split=split))
            )

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # Whatever you put in gen_kwargs will be passed to _generate_examples
                gen_kwargs={
                    "local_extracted_archive": local_extracted_archive["train"],
                    "audio_path": dl_manager.iter_archive(audio_path["train"]),
                    "metadata_path": metadata_path["train"],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "local_extracted_archive": local_extracted_archive["test"],
                    "audio_path": dl_manager.iter_archive(audio_path["test"]),
                    "metadata_path": metadata_path["test"],
                    "split": "test",
                },
            ),
        ]

    def _generate_examples(self, local_extracted_archive: Path, audio_path, metadata_path: Path, split: str) -> Tuple[int, Dict]:
        df = pd.read_csv(
            metadata_path,
            encoding="utf-8"
        )
        lang = self.config.subset_id.split("_")[-1]
        if lang != "indonesia":
            lang = _LANG_CODE[lang][0]
        path_to_audio = "librivox-indonesia"
        metadata = {}
        for id, row in df.iterrows():
            if lang == row["language"] or lang == "indonesia":
                path = os.path.join(path_to_audio, row["path"])
                metadata[path] = row
                metadata[path]["id"] = id

        for path, f in audio_path:
            if path in metadata:
                row = metadata[path]
                path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
                if self.config.schema == "source":
                    yield row["id"], {
                        "path": path,
                        "language": row["language"],
                        "reader": row["reader"],
                        "sentence": row["sentence"],
                        "audio": path,
                    }
                elif self.config.schema == "seacrowd_sptext":
                    yield row["id"], {
                        "id": row["id"],
                        "speaker_id": row["reader"],
                        "path": path,
                        "audio": path,
                        "text": row["sentence"],
                        "metadata": {
                            "speaker_age": None,
                            "speaker_gender": None,
                        }
                    }