Creating the repo structure

BSCs_Code_Switching_CA-ES_ASR_Test.py

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+from collections import defaultdict
+import os
+import json
+import csv
+import datasets
+
+_NAME="BSCs_Code_Switching_CA-ES_ASR_Test"
+_VERSION="1.0.0"
+
+_DESCRIPTION = """
+The BSC's Code-Switching Catalan-Spanish ASR Test is a speech dataset of 4 hours and 9 minutes. It consists of carefully selected recordings that feature code-switching between Catalan and Spanish. This dataset is designed to be a test set for Catalan ASR systems that need to handle code-switching to Spanish.
+"""
+
+_CITATION = """
+@misc{mena2025optimizing,
+      title={BSC's Code-Switching Catalan-Spanish ASR Test}, 
+      author={Hernández Mena, Carlos Daniel and Serra, Pol and Romero, Jacobo and Messaoudi, Abir and Giraldo, Jose and Armentano-Oller, Carme and Zevallos, Rodolfo and Meza, Ivan and Hernando, Javier},
+      organization={Barcelona Supercomputing Center},
+      year={2025},
+      url={https://huggingface.co/datasets/BSC-LT/BSCs_Code_Switching_CA-ES_ASR_Test},
+}
+"""
+
+_HOMEPAGE = "https://huggingface.co/BSC-LT"
+
+_LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/deed.en"
+
+_BASE_DATA_DIR = "corpus/"
+_METADATA  =  os.path.join(_BASE_DATA_DIR,"files", "metadata.tsv")
+
+_TARS  = os.path.join(_BASE_DATA_DIR,"files", "tars.paths")
+
+class BCSCSATConfig(datasets.BuilderConfig):
+    """BuilderConfig for BSC's Code-Switching Catalan-Spanish ASR Test"""
+
+    def __init__(self, name, **kwargs):
+        name=_NAME
+        super().__init__(name=name, **kwargs)
+
+class BCSCSAT(datasets.GeneratorBasedBuilder):
+    """BSC's Code-Switching Catalan-Spanish ASR Test"""
+
+    VERSION = datasets.Version(_VERSION)
+    BUILDER_CONFIGS = [
+        BCSCSATConfig(
+            name=_NAME,
+            version=datasets.Version(_VERSION),
+        )
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "audio_id": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16000),
+                "source": datasets.Value("string"),
+                "split": 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=dl_manager.download_and_extract(_METADATA)
+        
+        tars=dl_manager.download_and_extract(_TARS)
+        
+        hash_tar_files=defaultdict(dict)
+
+        with open(tars,'r') as f:
+            hash_tar_files['test']=[path.replace('\n','') for path in f]
+
+        hash_meta_paths={"test":metadata}
+        audio_paths = dl_manager.download(hash_tar_files)
+        
+        splits=["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.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 = ["source","split","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/files/tars.paths

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+corpus/speech/corts_valencianes_anonymized.tar.gz
+corpus/speech/parlament_parla.tar.gz