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""" NENA Speech Dataset""" |
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import csv |
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import os |
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import datasets |
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from tqdm import tqdm |
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from .dialects import DIALECTS |
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from .release_stats import STATS |
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_HOMEPAGE = "https://nena.ames.cam.ac.uk/" |
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_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/" |
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_BASE_URL = "https://huggingface.co/datasets/mnazari/nena_speech_1_0_test/resolve/main/" |
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_AUDIO_URL = _BASE_URL + "audio/{dialect}/{split}.tar" |
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_TRANSCRIPT_URL = _BASE_URL + "transcript/{dialect}/{split}.tsv" |
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import datasets |
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class NENASpeechConfig(datasets.BuilderConfig): |
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"""BuilderConfig for NENASpeech.""" |
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def __init__(self, name, version, **kwargs): |
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self.dialect = kwargs.pop("dialect", None) |
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self.release_date = kwargs.pop("release_date", None) |
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self.speakers=kwargs.pop("speakers", None) |
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self.total_examples=kwargs.pop("total_examples", None) |
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self.total_translated=kwargs.pop("total_translated", None) |
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self.total_labeled=kwargs.pop("total_labeled", None) |
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self.total_unlabeled=kwargs.pop("total_unlabeled", None) |
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description = ( |
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f"NENA Speech dataset in the {self.dialect} dialect released on {self.release_date}. " |
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f"The dataset currently consists of {self.total_unlabeled:.2f} minutes of unlabeled " |
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f"speech, {self.total_labeled:.2f} of transcribed speech, and {self.total_examples} " |
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f"multimodal translation examples. More examples are actively being crowdsourced." |
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) |
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super(NENASpeechConfig, self).__init__( |
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name=name, |
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version=datasets.Version(version), |
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description=description, |
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**kwargs, |
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) |
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class NENASpeech(datasets.GeneratorBasedBuilder): |
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DEFAULT_WRITER_BATCH_SIZE = 1000 |
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BUILDER_CONFIGS = [ |
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NENASpeechConfig( |
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name=dialect, |
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version=STATS["version"], |
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dialect=DIALECTS[dialect], |
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release_date=STATS["date"], |
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speakers=dialect_stats["speakers"], |
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total_examples=dialect_stats["totalExamples"], |
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total_translated=dialect_stats["examplesTranslated"], |
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total_labeled=dialect_stats["durationLabeled"] / 60, |
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total_unlabeled=dialect_stats["durationUnlabeled"] / 60, |
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) |
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for dialect, dialect_stats in STATS["dialects"].items() |
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] |
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def _info(self): |
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total_dialects = len(STATS["dialects"]) |
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total_examples = len(STATS["totalExamples"]) |
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total_labeled = len(STATS["durationLabeled"]) / 60 |
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total_unlabeled = len(STATS["durationUnlabeled"]) / 60 |
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description = ( |
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"NENA Speech is a multimodal dataset to help teach machines how real people speak " |
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"the Northeastern Neo-Aramaic dialects. The dataset currently consists of " |
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f"{total_unlabeled:.2f} minutes of unlabeled speech, {total_labeled:.2f} minutes of " |
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f"transcribed speech, and {total_examples} multimodal translation examples in " |
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f"{total_dialects} dialects. More examples are actively being crowdsourced." |
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) |
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features = datasets.Features( |
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{ |
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"transcription": datasets.Value("string"), |
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"translation": datasets.Value("string"), |
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"audio": datasets.features.Audio(sampling_rate=48_000), |
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"locale": datasets.Value("string"), |
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"proficiency": datasets.Value("string"), |
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"age": datasets.Value("string"), |
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"crowdsourced": datasets.Value("bool"), |
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"unlabeled": datasets.Value("bool"), |
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"interrupted": datasets.Value("bool"), |
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"client_id": datasets.Value("string"), |
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"path": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=description, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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features=features, |
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supervised_keys=None, |
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) |
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def _split_generators(self, dl_manager): |
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dialect = self.config.name |
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audio_urls = {} |
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splits = ("train", "dev", "test") |
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for split in splits: |
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audio_urls[split] = _AUDIO_URL.format(dialect=dialect, split=split) |
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archive_paths = dl_manager.download(audio_urls) |
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local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {} |
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meta_urls = {split: _TRANSCRIPT_URL.format(dialect=dialect, split=split) for split in splits} |
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meta_paths = dl_manager.download_and_extract(meta_urls) |
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split_generators = [] |
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split_names = { |
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"train": datasets.Split.TRAIN, |
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"dev": datasets.Split.VALIDATION, |
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"test": datasets.Split.TEST, |
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} |
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for split in splits: |
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split_generators.append( |
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datasets.SplitGenerator( |
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name=split_names.get(split, split), |
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gen_kwargs={ |
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"local_extracted_archive_paths": local_extracted_archive_paths.get(split), |
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"archive": dl_manager.iter_archive(archive_paths.get(split)), |
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"meta_path": meta_paths[split], |
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}, |
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), |
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) |
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return split_generators |
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def _generate_examples(self, local_extracted_archive_paths, archive, meta_path): |
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data_fields = list(self._info().features.keys()) |
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metadata = {} |
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with open(meta_path, encoding="utf-8") as f: |
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reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) |
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for row in tqdm(reader, desc="Reading metadata..."): |
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for field in data_fields: |
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if field not in row: |
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row[field] = "" |
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metadata[row["path"]] = row |
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for path, file in archive: |
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_, filename = os.path.split(path) |
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if filename in metadata: |
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result = dict(metadata[filename]) |
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path = os.path.join(local_extracted_archive_paths, path) if local_extracted_archive_paths else path |
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result["audio"] = {"path": path, "bytes": file.read()} |
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result["path"] = path |
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yield path, result |
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