Delete loading script

dimex100_light.py

@@ -1,122 +0,0 @@
-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()},
-                }