dimex100_light.py

from collections import defaultdict
import os
import json
import csv
import datasets

_NAME="dimex100_light"
_VERSION="1.0.0"

_DESCRIPTION = """
The DIMEx100 LIGHT Corpus is a reduced version of the DIMEx100 Adult Corpus, 
with the aim of facilitating the use of the DIMEx100 Corpus in various automatic 
speech recognition systems. DIMEx100 Adult Corpus was created by Dr. Luis Pineda 
from UNAM University at Mexico City.
"""

_CITATION = """
@misc{menadimex100light2017,
      title={DIMEx100 LIGHT CORPUS: Reduced version of the DIMEx100 Adult Corpus by Dr. Luis Pineda from UNAM University (Mexico).}, 
      author={Hernandez Mena, Carlos Daniel},
      year={2017},
      url={https://huggingface.co/datasets/carlosdanielhernandezmena/dimex100_light},
}
"""

_HOMEPAGE = "https://huggingface.co/datasets/carlosdanielhernandezmena/dimex100_light"

_LICENSE = "CC-BY-NC-ND-4.0, See https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en"

_BASE_DATA_DIR = "corpus/"
_METADATA_TRAIN  =  os.path.join(_BASE_DATA_DIR,"files", "metadata_train.tsv")

_TARS_TRAIN  = os.path.join(_BASE_DATA_DIR,"files", "tars_train.paths")

class Dimex100LightConfig(datasets.BuilderConfig):
    """BuilderConfig for DIMEx100 LIGHT CORPUS"""

    def __init__(self, name, **kwargs):
        name=_NAME
        super().__init__(name=name, **kwargs)

class Dimex100Light(datasets.GeneratorBasedBuilder):
    """DIMEx100 LIGHT CORPUS"""

    VERSION = datasets.Version(_VERSION)
    BUILDER_CONFIGS = [
        Dimex100LightConfig(
            name=_NAME,
            version=datasets.Version(_VERSION),
        )
    ]

    def _info(self):
        features = datasets.Features(
            {
                "audio_id": datasets.Value("string"),
                "audio": datasets.Audio(sampling_rate=16000),
                "speaker_id": datasets.Value("string"),
                "utterance_type": datasets.Value("string"),
                "gender": datasets.Value("string"),
                "duration": datasets.Value("float32"),
                "normalized_text": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):

        metadata_train=dl_manager.download_and_extract(_METADATA_TRAIN)
        
        tars_train=dl_manager.download_and_extract(_TARS_TRAIN)
        
        hash_tar_files=defaultdict(dict)

        with open(tars_train,'r') as f:
            hash_tar_files['train']=[path.replace('\n','') for path in f]

        hash_meta_paths={"train":metadata_train}
        audio_paths = dl_manager.download(hash_tar_files)
        
        splits=["train"]
        local_extracted_audio_paths = (
            dl_manager.extract(audio_paths) if not dl_manager.is_streaming else
            {
                split:[None] * len(audio_paths[split]) for split in splits
            }
        )                                                                                                            
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["train"]],
                    "local_extracted_archives_paths": local_extracted_audio_paths["train"],
                    "metadata_paths": hash_meta_paths["train"],
                }
            ),
        ]

    def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):
        
        features = ["speaker_id","utterance_type","gender","duration","normalized_text"]
        
        with open(metadata_paths) as f:
            metadata = {x["audio_id"]: x for x in csv.DictReader(f, delimiter="\t")}

        for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths):
            for audio_filename, audio_file in audio_archive:
                audio_id =os.path.splitext(os.path.basename(audio_filename))[0]
                path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename
                                        
                yield audio_id, {
                    "audio_id": audio_id,
                    **{feature: metadata[audio_id][feature] for feature in features},
                    "audio": {"path": path, "bytes": audio_file.read()},
                }