Convert dataset to Parquet (#1)

- Convert dataset to Parquet (c0b3d0b9b663b6a07dd323b6280c76e5e9997a79)
- Delete data file (53ea28961580631fff98f4ba960d3900896781dd)
- Delete loading script (d3a5f5f301c62bad5d6c12c46646aaa2e080a870)
- Delete data file (96773f4e97aae8f53c4f436fa2b55ad6cfda82bc)
- Delete data file (dcce09a1ec46d9a090c92b2974898518250af130)

README.md

@@ -1,28 +1,59 @@
 ---
 annotations_creators:
 - expert-generated
-language:
-- es
 language_creators:
 - other
+language:
+- es
 license:
 - cc-by-sa-4.0
 multilinguality:
 - monolingual
-pretty_name: 'CIEMPIESS FEM CORPUS: Audio and Transcripts of Female Speakers in Spanish.'
 size_categories:
 - 10K<n<100K
 source_datasets:
 - original
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+pretty_name: 'CIEMPIESS FEM CORPUS: Audio and Transcripts of Female Speakers in Spanish.'
 tags:
 - ciempiess
 - spanish
 - mexican spanish
 - ciempiess project
 - ciempiess-unam project
-task_categories:
-- automatic-speech-recognition
-task_ids: []
+dataset_info:
+  config_name: ciempiess_fem
+  features:
+  - name: audio_id
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 16000
+  - name: speaker_id
+    dtype: string
+  - name: gender
+    dtype: string
+  - name: duration
+    dtype: float32
+  - name: country
+    dtype: string
+  - name: normalized_text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 781435185.285
+    num_examples: 6505
+  download_size: 958145084
+  dataset_size: 781435185.285
+configs:
+- config_name: ciempiess_fem
+  data_files:
+  - split: train
+    path: ciempiess_fem/train-*
+  default: true
 ---
 
 # Dataset Card for ciempiess_fem

ciempiess_fem.py

@@ -1,124 +0,0 @@
-from collections import defaultdict
-import os
-import json
-import csv
-
-import datasets
-
-_NAME="ciempiess_fem"
-_VERSION="1.0.0"
-_AUDIO_EXTENSIONS=".flac"
-
-_DESCRIPTION = """
-The CIEMPIESS FEM Corpus was created by recordings and human transcripts of 21 different women. 16 of these women are mexican. The other ones come from Latin American countries.
-"""
-
-_CITATION = """
-@misc{carlosmenaciempiessfem2019,
-      title={CIEMPIESS FEM CORPUS: Audio and Transcripts of Female Speakers in Spanish.}, 
-      ldc_catalog_no={LDC2019S07},
-      DOI={https://doi.org/10.35111/xdx5-n815},
-      author={Hernandez Mena, Carlos Daniel},
-      journal={Linguistic Data Consortium, Philadelphia},
-      year={2019},
-      url={https://catalog.ldc.upenn.edu/LDC2019S07},
-}
-"""
-
-_HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC2019S07"
-
-_LICENSE = "CC-BY-SA-4.0, See https://creativecommons.org/licenses/by-sa/4.0/"
-
-_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 CiempiessFemConfig(datasets.BuilderConfig):
-    """BuilderConfig for CIEMPIESS FEM Corpus"""
-
-    def __init__(self, name, **kwargs):
-        name=_NAME
-        super().__init__(name=name, **kwargs)
-
-class CiempiessFem(datasets.GeneratorBasedBuilder):
-    """CIEMPIESS Fem Corpus"""
-
-    VERSION = datasets.Version(_VERSION)
-    BUILDER_CONFIGS = [
-        CiempiessFemConfig(
-            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"),
-                "gender": datasets.Value("string"),
-                "duration": datasets.Value("float32"),
-                "country": datasets.Value("string"),
-                "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","gender","duration","country","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 = audio_filename.split(os.sep)[-1].split(_AUDIO_EXTENSIONS)[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.tar.gz → ciempiess_fem/train-00000-of-00002.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2777f2b901562bb763789ef1c0243def54cc360dbb95c1f94de2b6dd638d435f
-size 958382502
+oid sha256:3c5583983650b4b9a4546d664bc2fdd5609fcb6b72cb254e16b1c9cc32925216
+size 464051187

ciempiess_fem/train-00001-of-00002.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:877411e4499c212c1364491430b0f6ead821dd69378615521338eb3de949cca0
+size 494093897

corpus/files/tars_train.paths

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