Convert dataset to Parquet

README.md

@@ -1,3 +1,42 @@
 ---
 license: cc-by-4.0
+dataset_info:
+  config_name: dummy_corpus_asr_es
+  features:
+  - name: audio_id
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 16000
+  - name: split
+    dtype: string
+  - name: gender
+    dtype: string
+  - name: normalized_text
+    dtype: string
+  - name: relative_path
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1557671.0
+    num_examples: 20
+  - name: validation
+    num_bytes: 974706.0
+    num_examples: 10
+  - name: test
+    num_bytes: 809111.0
+    num_examples: 10
+  download_size: 3351595
+  dataset_size: 3341488.0
+configs:
+- config_name: dummy_corpus_asr_es
+  data_files:
+  - split: train
+    path: dummy_corpus_asr_es/train-*
+  - split: validation
+    path: dummy_corpus_asr_es/validation-*
+  - split: test
+    path: dummy_corpus_asr_es/test-*
+  default: true
 ---

data/dev.tsv

@@ -1,11 +0,0 @@
-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

data/dev/female/common_voice_es_19666365.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

@@ -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},
-            }
-

data/dev/female/common_voice_es_19738526.flac → dummy_corpus_asr_es/test-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-size 126866
+oid sha256:9db40f91450f1013b6102930ebddec9e3a38baa48116dc0b62507457e1b28c53
+size 812764

data/dev/female/common_voice_es_19603417.flac → dummy_corpus_asr_es/train-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-oid sha256:e28b0b11f34663e7a34d104460082cb6e20697f332c1323096df40de82320ddb
-size 114168
+oid sha256:c3ff83c402ad880996bbbad66d2162ce6ba7f01fbb2116fa8d02bcab6c0b9fd0
+size 1560483

data/dev/female/common_voice_es_18549611.flac → dummy_corpus_asr_es/validation-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-oid sha256:ace1ffa77d3f0c5614ee84b1d17cd18aa0a09d6a47d079fde5780146b5fc8560
-size 89483
+oid sha256:3ef2dd44be92fc5f3206ba494e6429ac89529b8d49419143e466be99cc101301
+size 978348