Datasets:

TifinLab
/

kabyle_asr

@@ -1,116 +0,0 @@
-import csv
-import os
-import datasets
-from tqdm import tqdm
-import tarfile
-_CITATION = """\
-@inproceedings{commonvoice:2020,
-  author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
-  title = {Common Voice: A Massively-Multilingual Speech Corpus},
-  booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
-  pages = {4211--4215},
-  year = 2020
-}
-"""
-_HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets"
-_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
-_BASE_URL = "https://huggingface.co/datasets/TifinLab/kabyle_asr/raw/main/"
-_AUDIO_URL = _BASE_URL + "data/{split}.tar"
-_TRANSCRIPT_URL = _BASE_URL + "text/{split}.csv"
-class KabyleAsr(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.1.0")
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features({
-                "id": datasets.Value("int64"),
-                "path": datasets.Value("string"),
-                "audio": datasets.features.Audio(sampling_rate=48000),
-                "text": datasets.Value("string"),
-                "licence": datasets.Value("string"),
-            }),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-    def _split_generators(self, dl_manager):
-        splits = {
-            "train": _AUDIO_URL.format(split="train"),
-            "test": _AUDIO_URL.format(split="test"),
-        }
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"audio_paths": dl_manager.download(splits["train"]),
-                            "split":"train",
-                            "transcript_path": dl_manager.download_and_extract(_TRANSCRIPT_URL.format(split="train"))
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"audio_paths": dl_manager.download(splits["test"]),
-                            "split":"test",
-                            "transcript_path": dl_manager.download_and_extract(_TRANSCRIPT_URL.format(split="test"))},
-            ),
-        ]
-    def _generate_examples(self, audio_paths, split, transcript_path):
-        with open(transcript_path, encoding="utf-8") as f:
-            # L'utilisation de csv.DictReader permet de traiter facilement chaque ligne du CSV
-            reader = csv.DictReader(f, delimiter=";", quoting=csv.QUOTE_NONE)
-            # Extraction et traitement des fichiers audio de l'archive
-            audio_tar_path = audio_paths + ".tar"
-            with tarfile.open(audio_tar_path, "r") as tar:
-                # Créer un dictionnaire pour associer chaque fichier audio à son chemin extrait
-                audio_file_dict = {member.name: tar.extractfile(member) for member in tar.getmembers()}
-            # Itérer sur chaque ligne du fichier de métadonnées pour générer des exemples
-            for row_id, row in enumerate(tqdm(reader, desc=f"Génération d'exemples pour {split}")):
-                audio_filename = row["Path"]
-                sentence = row['Text']
-                licenseR = row['Licence']
-                # Vérifier si le fichier audio correspondant existe
-                if audio_filename in audio_file_dict:
-                    # Créer un chemin temporaire pour stocker le fichier audio extrait
-                    audio_extracted_path = os.path.join("temp", audio_filename)
-                    # Écrire le contenu du fichier audio extrait
-                    with open(audio_extracted_path, "wb") as audio_out:
-                        audio_out.write(audio_file_dict[audio_filename].read())
-                    with open(audio_extracted_path, "rb") as audio_in:
-                        audio_bytes = audio_in.read()
-                        yield row_id, {
-                            "id": row_id,
-                            "path": audio_filename,
-                            "audio": {"path": audio_extracted_path, "bytes": audio_bytes},
-                            "text": sentence,
-                            "licence": licenseR
-                        }