Convert dataset to Parquet (#1)

- Convert dataset to Parquet (329b1964b4dbf36e40934807c5dab1584e680e83)
- Delete loading script (ccf7cba3760cd2b90863852721995853a727b611)
- Delete data file (d269a28a78f83134f24c007035f4659547b3d2a3)
- Delete data file (6342c2cef7ee7f3ce1bd12985fd95e195cd4bbeb)
- Delete data file (01ce04da670b0d5e5313e4e81da23c65562a7dee)
- Delete data file (18332f08cb5112e47b0ffa01418c8166538dcc27)
- Delete data file (1c1da3469d26b7dfdacfdb086b342316511f23fd)
- Delete data file (48286381cb9147126fdd079d6b516226911ddbcd)
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README.md

@@ -1,3 +1,34 @@
 ---
 license: gpl-3.0
+dataset_info:
+  config_name: voxforge_spanish
+  features:
+  - name: audio_id
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 16000
+  - name: speaker_id
+    dtype: string
+  - name: country
+    dtype: string
+  - name: gender
+    dtype: string
+  - name: duration
+    dtype: float32
+  - name: normalized_text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3796458902.464
+    num_examples: 21692
+  download_size: 3441019616
+  dataset_size: 3796458902.464
+configs:
+- config_name: voxforge_spanish
+  data_files:
+  - split: train
+    path: voxforge_spanish/train-*
+  default: true
 ---

corpus/files/tars_train.paths

@@ -1,6 +0,0 @@
-corpus/speech/train/argentina.tar.gz
-corpus/speech/train/chile.tar.gz
-corpus/speech/train/latin_america.tar.gz
-corpus/speech/train/mexico.tar.gz
-corpus/speech/train/spain.tar.gz
-corpus/speech/train/unknown.tar.gz

corpus/speech/train/spain.tar.gz

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-size 2565077667

corpus/speech/train/unknown.tar.gz

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-oid sha256:22b01b9151ea36e0792021d5334fc5c85b7dc00eca8a0d6cc9169790e2cd1712
-size 76117697

voxforge_spanish.py

@@ -1,121 +0,0 @@
-from collections import defaultdict
-import os
-import json
-import csv
-import datasets
-
-_NAME="voxforge_spanish"
-_VERSION="1.0.0"
-
-_DESCRIPTION = """
-The VOXFORGE SPANISH CORPUS has a duration of 49 hours and it is constituted by read speech
-recorded by more than 2 thousand speakers. Most of the speakers contribute with 10 recordings 
-of approximately 10 seconds of duration each. 
-"""
-
-_CITATION = """
-@misc{menavoxforgespanish2017,
-      title={VOXFORGE SPANISH CORPUS: Audio and Transcripts in Spanish with a CIEMPIESS Corpus style, taken from voxforge.org}, 
-      author={Hernandez Mena, Carlos Daniel},
-      year={2017},
-      url={https://huggingface.co/ciempiess/voxforge_spanish},
-}
-"""
-
-_HOMEPAGE = "https://huggingface.co/ciempiess/voxforge_spanish"
-
-_LICENSE = "GPLv3, See https://www.gnu.org/licenses/gpl.txt"
-
-_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 VoxforgeSpanishConfig(datasets.BuilderConfig):
-    """BuilderConfig for VOXFORGE SPANISH CORPUS"""
-
-    def __init__(self, name, **kwargs):
-        name=_NAME
-        super().__init__(name=name, **kwargs)
-
-class VoxforgeSpanish(datasets.GeneratorBasedBuilder):
-    """VOXFORGE SPANISH CORPUS"""
-
-    VERSION = datasets.Version(_VERSION)
-    BUILDER_CONFIGS = [
-        VoxforgeSpanishConfig(
-            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"),
-                "country": 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","country","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()},
-                }

corpus/speech/train/latin_america.tar.gz → voxforge_spanish/train-00000-of-00008.parquet

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+size 453137913

corpus/speech/train/mexico.tar.gz → voxforge_spanish/train-00001-of-00008.parquet

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corpus/speech/train/argentina.tar.gz → voxforge_spanish/train-00002-of-00008.parquet

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corpus/speech/train/chile.tar.gz → voxforge_spanish/train-00003-of-00008.parquet

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voxforge_spanish/train-00004-of-00008.parquet

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voxforge_spanish/train-00005-of-00008.parquet

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voxforge_spanish/train-00007-of-00008.parquet

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