Delete loading script

dummy_corpus_asr_es.py

@@ -1,155 +0,0 @@
-from collections import defaultdict
-import os
-import json
-import csv
-
-import datasets
-
-_NAME="dummy_corpus_asr_es"
-_VERSION="1.0.0"
-
-_DESCRIPTION = """
-An extremely small corpus of 40 audio files taken from Common Voice (es) with the objective of testing how to share datasets in Hugging Face.
-"""
-
-_CITATION = """
-@misc{dummy-corpus-asr-es,
-      title={Dummy Corpus for ASR in Spanish.}, 
-      author={Hernandez Mena, Carlos Daniel},
-      year={2022},
-      url={https://huggingface.co/datasets/carlosdanielhernandezmena/dummy_corpus_asr_es},
-}
-"""
-
-_HOMEPAGE = "https://huggingface.co/datasets/carlosdanielhernandezmena/dummy_corpus_asr_es"
-
-_LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/"
-
-_BASE_DATA_DIR = "data/"
-_METADATA_TRAIN = _BASE_DATA_DIR + "train.tsv"
-_METADATA_TEST = _BASE_DATA_DIR + "test.tsv"
-_METADATA_DEV = _BASE_DATA_DIR + "dev.tsv"
-
-class DummyCorpusAsrEsConfig(datasets.BuilderConfig):
-    """BuilderConfig for Dummy Corpus ASR ES."""
-
-    def __init__(self, name, **kwargs):
-        name=_NAME
-        super().__init__(name=name, **kwargs)
-
-class DummyCorpusAsrEs(datasets.GeneratorBasedBuilder):
-    """The Dummy Corpus ASR ES dataset."""
-
-    VERSION = datasets.Version(_VERSION)
-    BUILDER_CONFIGS = [
-        DummyCorpusAsrEsConfig(
-            name=_NAME,
-            version=datasets.Version(_VERSION),
-        )
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "audio_id": datasets.Value("string"),
-                "audio": datasets.Audio(sampling_rate=16000),
-                "split": datasets.Value("string"),
-                "gender": datasets.Value("string"),
-                "normalized_text": datasets.Value("string"),
-                "relative_path": 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)
-        metadata_test=dl_manager.download_and_extract(_METADATA_TEST)
-        metadata_dev=dl_manager.download_and_extract(_METADATA_DEV)
-        
-        meta_paths={"train":metadata_train,"test":metadata_test,"dev":metadata_dev}
-        
-        with open(metadata_train) as f:
-            hash_meta_train = {x["audio_id"]: x for x in csv.DictReader(f, delimiter="\t")}
-
-        with open(metadata_test) as f:
-            hash_meta_test = {x["audio_id"]: x for x in csv.DictReader(f, delimiter="\t")}
-
-        with open(metadata_dev) as f:
-            hash_meta_dev = {x["audio_id"]: x for x in csv.DictReader(f, delimiter="\t")}            
-        
-        hash_audios=defaultdict(dict)
-        hash_audios["train"]=[]
-        for audio_in in hash_meta_train:
-            hash_audios["train"].append(hash_meta_train[audio_in]["relative_path"])
-
-        hash_audios["test"]=[]
-        for audio_in in hash_meta_test:
-            hash_audios["test"].append(hash_meta_test[audio_in]["relative_path"])
-        
-        hash_audios["dev"]=[]
-        for audio_in in hash_meta_dev:
-            hash_audios["dev"].append(hash_meta_dev[audio_in]["relative_path"])
-
-        relative_paths=hash_audios
-        
-        audio_paths = dl_manager.download(hash_audios)
-        
-        local_extracted_audio_paths = dl_manager.download_and_extract(audio_paths)
-
-        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": meta_paths["train"],
-                    "relative_paths":relative_paths["train"],
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["dev"]],
-                    "local_extracted_archives_paths": local_extracted_audio_paths["dev"],
-                    "metadata_paths": meta_paths["dev"],
-                    "relative_paths":relative_paths["dev"],
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["test"]],
-                    "local_extracted_archives_paths": local_extracted_audio_paths["test"],
-                    "metadata_paths": meta_paths["test"],
-                    "relative_paths":relative_paths["test"],
-                }
-            ),
-        ]
-
-    def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths,relative_paths):
-
-        features = ["normalized_text","gender","split","relative_path"]
-        
-        meta_path = metadata_paths
-
-        with open(meta_path) as f:
-            metadata = {x["audio_id"]: x for x in csv.DictReader(f, delimiter="\t")}
-
-        for audio_archive,local_path,rel_path in zip(audio_archives,local_extracted_archives_paths,relative_paths):
-            #audio_id = rel_path.split(os.sep)[-1].split(".flac")[0]
-            audio_id =os.path.splitext(os.path.basename(rel_path))[0]
-            path = local_path
-                        
-            yield audio_id, {
-                "audio_id": audio_id,
-                **{feature: metadata[audio_id][feature] for feature in features},
-                "audio": {"path": path},
-            }
-