Adding files to the repo for the first time

corpus/files/tars_train.paths

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

dimex100_light.py

@@ -0,0 +1,122 @@
+from collections import defaultdict
+import os
+import json
+import csv
+import datasets
+
+_NAME="dimex100_light"
+_VERSION="1.0.0"
+
+_DESCRIPTION = """
+The DIMEx100 LIGHT Corpus is a reduced version of the DIMEx100 Adult Corpus, 
+with the aim of facilitating the use of the DIMEx100 Corpus in various automatic 
+speech recognition systems. DIMEx100 Adult Corpus was created by Dr. Luis Pineda 
+from UNAM University at Mexico City.
+"""
+
+_CITATION = """
+@misc{menadimex100light2017,
+      title={DIMEx100 LIGHT CORPUS: Reduced version of the DIMEx100 Adult Corpus by Dr. Luis Pineda from UNAM University (Mexico).}, 
+      author={Hernandez Mena, Carlos Daniel},
+      year={2017},
+      url={https://huggingface.co/datasets/carlosdanielhernandezmena/dimex100_light},
+}
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/carlosdanielhernandezmena/dimex100_light"
+
+_LICENSE = "CC-BY-NC-ND-4.0, See https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en"
+
+_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 Dimex100LightConfig(datasets.BuilderConfig):
+    """BuilderConfig for DIMEx100 LIGHT CORPUS"""
+
+    def __init__(self, name, **kwargs):
+        name=_NAME
+        super().__init__(name=name, **kwargs)
+
+class Dimex100Light(datasets.GeneratorBasedBuilder):
+    """DIMEx100 LIGHT CORPUS"""
+
+    VERSION = datasets.Version(_VERSION)
+    BUILDER_CONFIGS = [
+        Dimex100LightConfig(
+            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"),
+                "utterance_type": 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","utterance_type","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()},
+                }