Delete Verbalex_voice.py

Verbalex_voice.py

@@ -1,134 +0,0 @@
-import csv
-import os
-
-import datasets
-from tqdm import tqdm
-
-from VerbaLex_Voice.accents import ACCENTS
-from VerbaLex_Voice.release_stats import STATS
-
-_HOMEPAGE = "https://huggingface.co/datasets/RitchieP/VerbaLex_voice"
-
-_LICENSE = "https://choosealicense.com/licenses/apache-2.0/"
-
-_BASE_URL = "https://huggingface.co/datasets/RitchieP/VerbaLex_voice/tree/main"
-
-_AUDIO_URL = _BASE_URL + "audio/{accent}/{split}/{accent}_{split}.tar"
-
-_TRANSCRIPT_URL = _BASE_URL + "transcript/{accent}/{split}.tsv"
-
-_CITATION = """\
-"""
-
-
-class VerbaLexVoiceConfig(datasets.BuilderConfig):
-    def __init__(self, name, version, **kwargs):
-        self.accent = kwargs.pop("accent", None)
-        self.num_speakers = kwargs.pop("num_speakers", None)
-        self.num_files = kwargs.pop("num_clips", None)
-        description = (
-            f"VerbaLex Voice english speech-to-text dataset in {self.accent} accent."
-        )
-
-        super(VerbaLexVoiceConfig, self).__init__(
-            name=name,
-            version=datasets.Version(version),
-            description=description,
-            **kwargs,
-        )
-
-
-class VerbaLexVoiceDataset(datasets.GeneratorBasedBuilder):
-    """
-    VerbaLex is a dataset containing different English accents from non-native English speakers.
-    This dataset is created directly from the L2-Arctic dataset.
-    """
-    BUILDER_CONFIGS = [
-        VerbaLexVoiceConfig(
-            name=accent,
-            version=STATS["version"],
-            accent=ACCENTS[accent],
-            num_speakers=accent_stats["numOfSpeaker"],
-            num_files=accent_stats["numOfWavFiles"]
-        )
-        for accent, accent_stats in STATS["accents"].items()
-    ]
-
-    DEFAULT_CONFIG_NAME = "all"
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=(
-                "VerbaLex Voice is a speech dataset focusing on accented English speech."
-                "It specifically targets speeches from speakers that is a non-native English speaker."
-            ),
-            features=datasets.Features(
-                {
-                    "path": datasets.Value("string"),
-                    "accent": datasets.Value("string"),
-                    "sentence": datasets.Value("string"),
-                    "audio": datasets.Audio(sampling_rate=44_100)
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators"""
-        accent = self.config.name
-
-        splits = ("train", "test")
-        audio_urls = {}
-        for split in splits:
-            audio_urls[split] = _AUDIO_URL.format(accent=accent, split=split)
-        archive_paths = dl_manager.download(audio_urls)
-        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
-
-        meta_urls = {split: _TRANSCRIPT_URL.format(accent=accent, split=split) for split in splits}
-        meta_paths = dl_manager.download_and_extract(meta_urls)
-
-        split_names = {
-            "train": datasets.Split.TRAIN,
-            "test": datasets.Split.TEST
-        }
-        split_generators = []
-        for split in splits:
-            split_generators.append(
-                datasets.SplitGenerator(
-                    name=split_names.get(split, split),
-                    gen_kwargs={
-                        "local_extracted_archive_paths": local_extracted_archive_paths.get(split),
-                        "archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
-                        "meta_path": meta_paths[split]
-                    }
-                )
-            )
-
-        return split_generators
-
-    def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
-        data_fields = list(self._info().features.keys())
-        metadata = {}
-        with open(meta_path, encoding="UTF-8") as f:
-            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-            for row in tqdm(reader, desc="Reading metadata..."):
-                if not row["path"].endswith(".wav"):
-                    row["path"] += ".wav"
-                for field in data_fields:
-                    if field not in row:
-                        row[field] = ""
-                metadata[row["path"]] = row
-
-        for i, audio_archive in enumerate(archives):
-            for path, file in audio_archive:
-                _, filename = os.path.split(path)
-                if filename in metadata:
-                    result = dict(metadata[filename])
-                    path = os.path.join(local_extracted_archive_paths[i],
-                                        path) if local_extracted_archive_paths else path
-                    result["audio"] = {"path": path, "bytes": file.read()}
-                    result["path"] = path
-                    yield path, result