Upload VerbaLex_voice.py

VerbaLex_voice.py

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+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