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from collections import defaultdict |
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import os |
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import glob |
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import csv |
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from tqdm.auto import tqdm |
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import datasets |
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_DESCRIPTION = """ |
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A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation. |
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""" |
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_CITATION = """ |
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@inproceedings{wang-etal-2021-voxpopuli, |
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title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, |
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Semi-Supervised Learning and Interpretation", |
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author = "Wang, Changhan and |
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Riviere, Morgane and |
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Lee, Ann and |
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Wu, Anne and |
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Talnikar, Chaitanya and |
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Haziza, Daniel and |
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Williamson, Mary and |
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Pino, Juan and |
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Dupoux, Emmanuel", |
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booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics |
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and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", |
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month = aug, |
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year = "2021", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.acl-long.80", |
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doi = "10.18653/v1/2021.acl-long.80", |
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pages = "993--1003", |
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} |
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""" |
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_HOMEPAGE = "https://github.com/facebookresearch/voxpopuli" |
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_LICENSE = "CC0, also see https://www.europarl.europa.eu/legal-notice/en/" |
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_LANGUAGES = sorted( |
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[ |
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"en", "de", "fr", "es", "pl", "it", "ro", "hu", "cs", "nl", "fi", "hr", |
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"sk", "sl", "et", "lt", "pt", "bg", "el", "lv", "mt", "sv", "da" |
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] |
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) |
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_LANGUAGES_V2 = [f"{x}_v2" for x in _LANGUAGES] |
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_ASR_LANGUAGES = [ |
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"en", "de", "fr", "es", "pl", "it", "ro", "hu", "cs", "nl", "fi", "hr", |
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"sk", "sl", "et", "lt" |
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] |
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_ASR_ACCENTED_LANGUAGES = [ |
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"en_accented" |
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] |
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_YEARS = list(range(2009, 2020 + 1)) |
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_CONFIG_TO_LANGS = { |
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"400k": _LANGUAGES, |
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"100k": _LANGUAGES, |
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"10k": _LANGUAGES, |
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"asr": _ASR_LANGUAGES, |
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} |
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_CONFIG_TO_YEARS = { |
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"400k": _YEARS + [f"{y}_2" for y in _YEARS], |
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"100k": _YEARS, |
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"10k": [2019, 2020], |
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"asr": _YEARS, |
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} |
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for lang in _LANGUAGES: |
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_CONFIG_TO_YEARS[lang] = _YEARS |
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for lang in _LANGUAGES_V2: |
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_CONFIG_TO_YEARS[lang] = _YEARS + [f"{y}_2" for y in _YEARS] |
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_BASE_URL = "https://dl.fbaipublicfiles.com/voxpopuli/" |
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_DATA_URL = _BASE_URL + "audios/{lang}_{year}.tar" |
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_ASR_DATA_URL = _BASE_URL + "audios/original_{year}.tar" |
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_UNLABELLED_META_URL = _BASE_URL + "annotations/unlabelled_v2.tsv.gz" |
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_ASR_META_URL = _BASE_URL + "annotations/asr/asr_{lang}.tsv.gz" |
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class VoxpopuliConfig(datasets.BuilderConfig): |
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"""BuilderConfig for VoxPopuli.""" |
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def __init__(self, name, **kwargs): |
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""" |
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Args: |
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name: `string`, name of dataset config |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__(name=name, **kwargs) |
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name = name.split("_")[0] |
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self.languages = [name] if name in _LANGUAGES else _CONFIG_TO_LANGS[name] |
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self.years = _CONFIG_TO_YEARS[name] |
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class Voxpopuli(datasets.GeneratorBasedBuilder): |
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"""The VoxPopuli dataset.""" |
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VERSION = datasets.Version("1.3.0") |
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BUILDER_CONFIGS = [ |
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VoxpopuliConfig( |
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name=name, |
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version=datasets.Version("1.3.0"), |
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) |
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for name in _LANGUAGES + _LANGUAGES_V2 + ["10k", "100k", "400k"] |
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] |
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DEFAULT_WRITER_BATCH_SIZE = 256 |
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def _info(self): |
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try: |
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import torch |
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import torchaudio |
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except ImportError as e: |
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raise ValueError( |
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f"{str(e)}.\n" + |
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"Loading voxpopuli requires `torchaudio` to be installed." |
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"You can install torchaudio with `pip install torchaudio`." |
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) |
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global torchaudio |
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features = datasets.Features( |
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{ |
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"path": datasets.Value("string"), |
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"language": datasets.ClassLabel(names=_LANGUAGES), |
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"year": datasets.Value("int16"), |
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"audio": datasets.Audio(sampling_rate=16_000), |
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"segment_id": datasets.Value("int16"), |
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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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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _read_metadata_unlabelled(self, metadata_path): |
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def predicate(id_): |
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is_plenary = id_.find("PLENARY") > -1 |
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if self.config.name == "10k": |
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return is_plenary and 20190101 <= int(id_[:8]) < 20200801 |
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elif self.config.name == "100k": |
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return is_plenary |
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elif self.config.name in _LANGUAGES: |
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return is_plenary and id_.endswith(self.config.name) |
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elif self.config.name in _LANGUAGES_V2: |
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return id_.endswith(self.config.name.split("_")[0]) |
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return True |
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metadata = defaultdict(list) |
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with open(metadata_path, encoding="utf-8") as csv_file: |
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csv_reader = csv.reader(csv_file, delimiter="\t") |
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for i, row in tqdm(enumerate(csv_reader)): |
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if i == 0: |
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continue |
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event_id, segment_id, start, end = row |
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_, lang = event_id.rsplit("_", 1)[-2:] |
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if lang in self.config.languages and predicate(event_id): |
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metadata[event_id].append((float(start), float(end))) |
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return metadata |
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def _read_metadata_asr(self, metadata_paths): |
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pass |
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def _split_generators(self, dl_manager): |
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metadata_path = dl_manager.download_and_extract(_UNLABELLED_META_URL) |
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urls = [_DATA_URL.format(lang=language, year=year) for language in self.config.languages for year in self.config.years] |
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dl_manager.download_config.num_proc = len(urls) |
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data_dirs = dl_manager.download_and_extract(urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"data_dirs": data_dirs, |
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"metadata_path": metadata_path, |
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} |
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), |
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] |
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def _generate_examples(self, data_dirs, metadata_path): |
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metadata = self._read_metadata_unlabelled(metadata_path) |
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for data_dir in data_dirs: |
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for file in glob.glob(f"{data_dir}/**/*.ogg", recursive=True): |
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path_components = file.split(os.sep) |
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language, year, audio_filename = path_components[-3:] |
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audio_id, _ = os.path.splitext(audio_filename) |
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if audio_id not in metadata: |
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continue |
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timestamps = metadata[audio_id] |
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waveform, sr = torchaudio.load(file) |
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duration = waveform.size(1) |
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for segment_id, (start, stop) in enumerate(timestamps): |
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segment = waveform[:, int(start * sr): min(int(stop * sr), duration)] |
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yield f"{audio_filename}_{segment_id}", { |
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"path": file, |
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"language": language, |
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"year": year, |
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"audio": { |
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"array": segment[0], |
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"sampling_rate": 16_000 |
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}, |
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"segment_id": segment_id, |
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} |
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