Convert dataset to Parquet

README.md

@@ -1,5 +1,42 @@
 ---
 license: cc-by-4.0
+dataset_info:
+  config_name: toy_corpus_asr_ca
+  features:
+  - name: audio_id
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 16000
+  - name: language
+    dtype: string
+  - name: normalized_text
+    dtype: string
+  - name: absolute_path
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 871036.0
+    num_examples: 20
+  - name: validation
+    num_bytes: 370873.0
+    num_examples: 10
+  - name: test
+    num_bytes: 416706.0
+    num_examples: 10
+  download_size: 1639360
+  dataset_size: 1658615.0
+configs:
+- config_name: toy_corpus_asr_ca
+  data_files:
+  - split: train
+    path: toy_corpus_asr_ca/train-*
+  - split: validation
+    path: toy_corpus_asr_ca/validation-*
+  - split: test
+    path: toy_corpus_asr_ca/test-*
+  default: true
 ---
 
 This is an example of a repository with a standard data loader. The audio files are compressed in tar format. Since this repository contains very few audio files, it can be used to test certain scripts in local machines.

corpus/files/metadata_dev.tsv

@@ -1,11 +0,0 @@
-audio_id	language	normalized_text	absolute_path
-common_voice_ca_34546728	Catalan	un nen petit escolta un telèfon mòbil	corpus/speech/dev/common_voice_ca_34546728.mp3
-common_voice_ca_31490843	Catalan	dues dones parlen per telèfon mòbil mentre seuen a unes escales de pedra	corpus/speech/dev/common_voice_ca_31490843.mp3
-common_voice_ca_30835494	Catalan	enviem aquí la notificació digital	corpus/speech/dev/common_voice_ca_30835494.mp3
-common_voice_ca_17664763	Catalan	tributs cedits per l'estat respecte dels quals aquest comparteix competències inspectores amb la generalitat	corpus/speech/dev/common_voice_ca_17664763.mp3
-common_voice_ca_17567381	Catalan	la longitud del tronc de l'actuació és de sis mil sis-cents seixanta-huit coma huit-cents setanta-huit metres	corpus/speech/dev/common_voice_ca_17567381.mp3
-common_voice_ca_28791266	Catalan	escalar l'everest era l'ambició de la seva vida	corpus/speech/dev/common_voice_ca_28791266.mp3
-common_voice_ca_17463398	Catalan	tots els homes són iguals	corpus/speech/dev/common_voice_ca_17463398.mp3
-common_voice_ca_31062549	Catalan	he estat de nou amb el teu amic bart	corpus/speech/dev/common_voice_ca_31062549.mp3
-common_voice_ca_32094808	Catalan	el vint-i-nou de febrer en roger anirà al metge	corpus/speech/dev/common_voice_ca_32094808.mp3
-common_voice_ca_17468207	Catalan	on són els altres	corpus/speech/dev/common_voice_ca_17468207.mp3

corpus/files/metadata_test.tsv

@@ -1,11 +0,0 @@
-audio_id	language	normalized_text	absolute_path
-common_voice_ca_17517643	Catalan	ho he vist	corpus/speech/test/common_voice_ca_17517643.mp3
-common_voice_ca_26260354	Catalan	sota aquest segell van publicar un llibre dels itineraris de rhode island	corpus/speech/test/common_voice_ca_26260354.mp3
-common_voice_ca_37597161	Catalan	actualment només queda el de l'escola	corpus/speech/test/common_voice_ca_37597161.mp3
-common_voice_ca_31310653	Catalan	déu va crear aleshores una dona coneguda com sela perquè fos la seva dona	corpus/speech/test/common_voice_ca_31310653.mp3
-common_voice_ca_31118063	Catalan	l'emir va arribar fins a pamplona i la va destruir totalment	corpus/speech/test/common_voice_ca_31118063.mp3
-common_voice_ca_19026173	Catalan	cada dia que passa quimet està més satisfet del seu ordinador i de la seua impressora de color	corpus/speech/test/common_voice_ca_19026173.mp3
-common_voice_ca_17468208	Catalan	softcatalà recomana executar primer el catalanitzador a l'equip i posteriorment instal·lar-hi el libreoffice	corpus/speech/test/common_voice_ca_17468208.mp3
-common_voice_ca_37590278	Catalan	la modalitat de justificació la determina l'import de la subvenció atorgada	corpus/speech/test/common_voice_ca_37590278.mp3
-common_voice_ca_30771720	Catalan	el dia del judici	corpus/speech/test/common_voice_ca_30771720.mp3
-common_voice_ca_30886886	Catalan	tota l'estona hem anat amb els esquís posats	corpus/speech/test/common_voice_ca_30886886.mp3

corpus/files/metadata_train.tsv

@@ -1,21 +0,0 @@
-audio_id	language	normalized_text	absolute_path
-common_voice_ca_32391864	Catalan	per cap d'any na neida i na bet volen anar al cinema	corpus/speech/train/common_voice_ca_32391864.mp3
-common_voice_ca_17459145	Catalan	després va explicar per què era allí	corpus/speech/train/common_voice_ca_17459145.mp3
-common_voice_ca_17376998	Catalan	difon entre els auditors els criteris aplicables a l'hora de fer les inspeccions i controls de les entitats	corpus/speech/train/common_voice_ca_17376998.mp3
-common_voice_ca_17492886	Catalan	gràcies per venir	corpus/speech/train/common_voice_ca_17492886.mp3
-common_voice_ca_20754772	Catalan	molts fans consideren que l'estil propi de la banda definitivament es forja en aquest disc	corpus/speech/train/common_voice_ca_20754772.mp3
-common_voice_ca_19035756	Catalan	fes bé que no ho perdràs	corpus/speech/train/common_voice_ca_19035756.mp3
-common_voice_ca_33660753	Catalan	gens brillant sobretot pel rei mort	corpus/speech/train/common_voice_ca_33660753.mp3
-common_voice_ca_19433813	Catalan	la distribució és oberta amb un porxo perimetral exterior	corpus/speech/train/common_voice_ca_19433813.mp3
-common_voice_ca_31397205	Catalan	àrees organitzades d'activitat de tempestes reforcen zones frontals preexistents i poden ultrapassar fronts freds	corpus/speech/train/common_voice_ca_31397205.mp3
-common_voice_ca_35110415	Catalan	envoltant el talaiot trobem un mur de construcció moderna que oculta parcialment el talaiot originari	corpus/speech/train/common_voice_ca_35110415.mp3
-common_voice_ca_17513482	Catalan	els amics te'ls tries els parents te'ls tragues	corpus/speech/train/common_voice_ca_17513482.mp3
-common_voice_ca_20187488	Catalan	només l'educació primària és gratuïta a atakora	corpus/speech/train/common_voice_ca_20187488.mp3
-common_voice_ca_31834990	Catalan	dissabte na dolça i en marc aniran a la biblioteca	corpus/speech/train/common_voice_ca_31834990.mp3
-common_voice_ca_17641708	Catalan	el discurs econòmic de les principals formacions polítiques és dèbil poc estructurat i excessivament superficial	corpus/speech/train/common_voice_ca_17641708.mp3
-common_voice_ca_19039808	Catalan	s'hi farà un tast del nou firefox tres i qui s'acosti podrà gaudir també d'obsequis commemoratius	corpus/speech/train/common_voice_ca_19039808.mp3
-common_voice_ca_30886885	Catalan	la malaltia li ha descarnat les galtes	corpus/speech/train/common_voice_ca_30886885.mp3
-common_voice_ca_17475567	Catalan	una vegada teníem clar què volíem tocava estructurar-ho	corpus/speech/train/common_voice_ca_17475567.mp3
-common_voice_ca_31500541	Catalan	prades o almenys el seu castell té un origen islàmic	corpus/speech/train/common_voice_ca_31500541.mp3
-common_voice_ca_19895047	Catalan	no obstant això és considerat el campionat nacional paraguaià	corpus/speech/train/common_voice_ca_19895047.mp3
-common_voice_ca_17475568	Catalan	mar bonança mar de dones	corpus/speech/train/common_voice_ca_17475568.mp3

corpus/files/tars_dev.paths

@@ -1 +0,0 @@
-corpus/speech/dev.tar.gz

corpus/files/tars_test.paths

@@ -1 +0,0 @@
-corpus/speech/test.tar.gz

corpus/files/tars_train.paths

@@ -1 +0,0 @@
-corpus/speech/train.tar.gz

toy_corpus_asr_ca.py

@@ -1,148 +0,0 @@
-from collections import defaultdict
-import os
-import json
-import csv
-
-import datasets
-
-_NAME="toy_corpus_asr_ca"
-_VERSION="1.0.0"
-_AUDIO_EXTENSIONS=".mp3"
-
-_DESCRIPTION = """
-An extremely small corpus of 40 audio files taken from Common Voice (ca) with the objective of testing how to share datasets in Hugging Face.
-"""
-
-_CITATION = """
-@misc{toy_corpus_asr_es,
-      title={Toy Corpus for ASR in Catalan.}, 
-      author={Hernandez Mena, Carlos Daniel},
-      year={2024},
-      url={https://huggingface.co/datasets/carlosdanielhernandezmena/toy_corpus_asr_ca},
-}
-"""
-
-_HOMEPAGE = "https://huggingface.co/datasets/carlosdanielhernandezmena/toy_corpus_asr_ca"
-
-_LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/"
-
-_BASE_DATA_DIR = "corpus/"
-_METADATA_TRAIN =  os.path.join(_BASE_DATA_DIR,"files","metadata_train.tsv")
-_METADATA_TEST  =  os.path.join(_BASE_DATA_DIR,"files", "metadata_test.tsv")
-_METADATA_DEV   =  os.path.join(_BASE_DATA_DIR,"files",  "metadata_dev.tsv")
-
-_TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files","tars_train.paths")
-_TARS_TEST  = os.path.join(_BASE_DATA_DIR,"files", "tars_test.paths")
-_TARS_DEV   = os.path.join(_BASE_DATA_DIR,"files",  "tars_dev.paths")
-
-class ToyCorpusAsrEsConfig(datasets.BuilderConfig):
-    """BuilderConfig for Toy Corpus ASR CA."""
-
-    def __init__(self, name, **kwargs):
-        name=_NAME
-        super().__init__(name=name, **kwargs)
-
-class ToyCorpusAsrEs(datasets.GeneratorBasedBuilder):
-    """The Toy Corpus ASR CA dataset."""
-
-    VERSION = datasets.Version(_VERSION)
-    BUILDER_CONFIGS = [
-        ToyCorpusAsrEsConfig(
-            name=_NAME,
-            version=datasets.Version(_VERSION),
-        )
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {                
-                "audio_id": datasets.Value("string"),
-                "audio": datasets.Audio(sampling_rate=16000),
-                "language": datasets.Value("string"),
-                "normalized_text": datasets.Value("string"),
-                "absolute_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)   
-        
-        tars_train=dl_manager.download_and_extract(_TARS_TRAIN)
-        tars_test=dl_manager.download_and_extract(_TARS_TEST)
-        tars_dev=dl_manager.download_and_extract(_TARS_DEV)
-        
-        hash_tar_files=defaultdict(dict)
-        with open(tars_train,'r') as f:
-            hash_tar_files['train']=[path.replace('\n','') for path in f]
-
-        with open(tars_test,'r') as f:
-            hash_tar_files['test']=[path.replace('\n','') for path in f]
-            
-        with open(tars_dev,'r') as f:
-            hash_tar_files['dev']=[path.replace('\n','') for path in f]
-    
-        hash_meta_paths={"train":metadata_train,"test":metadata_test,"dev":metadata_dev}
-        audio_paths = dl_manager.download(hash_tar_files)
-        
-        splits=["train","dev","test"]
-        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"],
-                }
-            ),
-            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": hash_meta_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": hash_meta_paths["test"],
-                }
-            ),
-        ]
-
-    def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):
-
-        features = ["language","normalized_text","absolute_path"]
-        
-        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 = audio_filename.split(os.sep)[-1].split(_AUDIO_EXTENSIONS)[0]
-                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()},
-                }

corpus/speech/dev.tar.gz → toy_corpus_asr_ca/test-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-size 360068
+oid sha256:6165b90eff89130a3a85444486fd265d24ff50a21e082aef919a0d5a6ebaa1d8
+size 393461

corpus/speech/test.tar.gz → toy_corpus_asr_ca/train-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-size 379065
+oid sha256:e61010761176ee11fcc7a18925f39faac8206cb45fa84775c0fbce2c939ba4d7
+size 872965

corpus/speech/train.tar.gz → toy_corpus_asr_ca/validation-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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