Adding all the files for the first time

data/dev.tsv

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+audio_id	split	gender	normalized_text	relative_path
+common_voice_es_18328658	dev	male	ni siquiera pienses en eso vaquero o sí muchacho rudo	data/dev/male/common_voice_es_18328658.flac
+common_voice_es_18549611	dev	female	invitados del cumpleaños a las tres en punto cálmense todos	data/dev/female/common_voice_es_18549611.flac
+common_voice_es_19026674	dev	male	en la supervivencia de la especie	data/dev/male/common_voice_es_19026674.flac
+common_voice_es_19159450	dev	male	antes de la llegada de los europeos era fue una potencia política en halmahera	data/dev/male/common_voice_es_19159450.flac
+common_voice_es_19597257	dev	male	la liga quedó interrumpida en los últimos años de la segunda guerra mundial	data/dev/male/common_voice_es_19597257.flac
+common_voice_es_19603417	dev	female	desde entonces ha participado en numerosas novilladas sin picadores	data/dev/female/common_voice_es_19603417.flac
+common_voice_es_19604077	dev	male	apareció en la película toma estas alas rotas	data/dev/male/common_voice_es_19604077.flac
+common_voice_es_19666365	dev	female	sin embargo a lo largo del capítulo subyace una gran pregunta	data/dev/female/common_voice_es_19666365.flac
+common_voice_es_19738526	dev	female	fue su reino en su tiempo el más poderoso e influyente de europa occidental	data/dev/female/common_voice_es_19738526.flac
+common_voice_es_19942148	dev	female	en las últimas versiones de final cut pro apple inc	data/dev/female/common_voice_es_19942148.flac

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data/test.tsv

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+audio_id	split	gender	normalized_text	relative_path
+common_voice_es_18307761	test	male	y calló tal vez esperando una disculpa amante pero yo preferí guardar silencio	data/test/male/common_voice_es_18307761.flac
+common_voice_es_18569707	test	male	esos hombres son profesionales son los mejores	data/test/male/common_voice_es_18569707.flac
+common_voice_es_18831947	test	male	uno levanta la caza y otro la mata	data/test/male/common_voice_es_18831947.flac
+common_voice_es_19131994	test	female	ante esta situación el oficial mandó prender fuego a la casa	data/test/female/common_voice_es_19131994.flac
+common_voice_es_19523672	test	male	fue vicario apostólico del uruguay	data/test/male/common_voice_es_19523672.flac
+common_voice_es_19610763	test	male	se desempeñó como mediocampista ofensivo	data/test/male/common_voice_es_19610763.flac
+common_voice_es_19629310	test	female	esta pieza se retira del paciente junto con la aguja	data/test/female/common_voice_es_19629310.flac
+common_voice_es_19699841	test	female	el grupo que acabe antes será el ganador	data/test/female/common_voice_es_19699841.flac
+common_voice_es_19960886	test	female	el gobierno estadounidense no la reconoció como representante del pueblo cubano	data/test/female/common_voice_es_19960886.flac
+common_voice_es_20260627	test	female	fue redactor en jefe de la página deportiva de el diario el país	data/test/female/common_voice_es_20260627.flac

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data/train.tsv

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+audio_id	split	gender	normalized_text	relative_path
+common_voice_es_18306568	train	male	no me hables acerca de importancia	data/train/male/common_voice_es_18306568.flac
+common_voice_es_18320698	train	male	he restringido el acceso a la cubierta superior	data/train/male/common_voice_es_18320698.flac
+common_voice_es_19596707	train	male	los orígenes precisos de the hood son desconocidos	data/train/male/common_voice_es_19596707.flac
+common_voice_es_20501506	train	male	una vez destruidos se pierden inexorablemente	data/train/male/common_voice_es_20501506.flac
+common_voice_es_20507096	train	male	el resto están todas en inglaterra	data/train/male/common_voice_es_20507096.flac
+common_voice_es_20511043	train	male	las canciones de los comerciales sirvieron como singles para lee entre sus álbumes	data/train/male/common_voice_es_20511043.flac
+common_voice_es_20711016	train	female	los hombres se quedan a jugar a las cartas hasta que amanece sin dormir	data/train/female/common_voice_es_20711016.flac
+common_voice_es_20711252	train	female	con trece años comienza a realizar pequeños papeles para series de televisión y cine	data/train/female/common_voice_es_20711252.flac
+common_voice_es_20717545	train	female	su actividad fuera del campo fue variada	data/train/female/common_voice_es_20717545.flac
+common_voice_es_20717953	train	female	actualmente no está activo	data/train/female/common_voice_es_20717953.flac
+common_voice_es_20722210	train	female	es nativo de farmington hills michigan	data/train/female/common_voice_es_20722210.flac
+common_voice_es_20722849	train	female	incluye el navegador web safari y la aplicación mail de apple	data/train/female/common_voice_es_20722849.flac
+common_voice_es_20724424	train	female	desempeña un papel vital en la promoción del talento nacional e internacional emergente	data/train/female/common_voice_es_20724424.flac
+common_voice_es_20725001	train	female	las grabaciones se extendieron hasta finales del mes de mayo	data/train/female/common_voice_es_20725001.flac
+common_voice_es_20931278	train	female	entre las personas nombradas se encontraba carlos alberto lacoste	data/train/female/common_voice_es_20931278.flac
+common_voice_es_20932253	train	female	hasta el presente ningún vehículo orbital reutilizable real se ha llegado a usar	data/train/female/common_voice_es_20932253.flac
+common_voice_es_21469616	train	male	sus principales producciones fueron escritas para ser representadas ó cantadas	data/train/male/common_voice_es_21469616.flac
+common_voice_es_21486095	train	male	permite eliminar los reflejos luminosos y obtener efectos con las sombras	data/train/male/common_voice_es_21486095.flac
+common_voice_es_21543608	train	male	el número y clase de pájaros varía en cada nivel	data/train/male/common_voice_es_21543608.flac
+common_voice_es_21580953	train	male	la parte norte del brocal es más irregular con una protuberancia hacia el norte noroeste	data/train/male/common_voice_es_21580953.flac

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dummy_corpus_asr_es.py

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+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"])
+
+        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": {
+                        "train": [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"], "split":"train",
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "audio_archives": {
+                        "dev":[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"], "split":"dev",
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "audio_archives": {
+                        "test":[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"], "split":"test",
+                }
+            ),
+        ]
+
+    def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths,split):
+
+        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_extracted_archive_path in zip(audio_archives[split], local_extracted_archives_paths):
+            audio_id = local_extracted_archive_path.split(os.sep)[-1].split(".flac")[0]
+            path = local_extracted_archive_path
+            
+            yield audio_id, {
+                "audio_id": audio_id,
+                **{feature: metadata[audio_id][feature] for feature in features},
+                "audio": {"path": path},
+            }
+