Upload dataset scripts
Browse files- accents.py +4 -0
- generate_train_test_split.py +108 -0
- release_stats.py +8 -0
- verbalex_voice.py +134 -0
accents.py
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ACCENTS = {
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"ar": "arabic",
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"zh": "chinese"
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}
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generate_train_test_split.py
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# This file is used to generate the train and test split of files within
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# the file system.
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import os
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import re
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import shutil
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import tarfile
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from math import floor
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from random import shuffle
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def get_file_list_from_dir(data_dir):
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"""Get a list of files within the directory"""
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all_files = os.listdir(os.path.abspath(data_dir))
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data_files = list(filter(lambda file: file.endswith('.wav'), all_files))
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return data_files
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# Due to the audio and transcript files are in different folders, we need to split
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# them into train and test splits while maintaining their correct mapping in both splits
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# For example, if transcript 0001 is in test split, audio 0001 should also be in test split.
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#
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# Hence, instead of splitting the files itself, I will split the files based on their
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# ID number. Because every transcript and audio file has an ID number.
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# For example, arctic_a0001.txt is the transcript for arctic_a0001.wav.
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#
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# After splitting the ID numbers, we will then assign the files to their respective splits
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# based on their ID numbers.
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def shuffle_file_id(files):
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file_id_regex = re.compile(r'[a-z]\d\d\d\d')
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file_id = []
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for file in files:
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file_id.append(file_id_regex.search(file).group())
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shuffle(file_id)
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return file_id
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def train_test_splits(file_ids, training_ratio):
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split=training_ratio
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split_index = floor(len(file_ids) * split)
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train = file_ids[:split_index]
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test = file_ids[split_index:]
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return train, test
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if __name__ == "__main__":
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print("Running")
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audio_file_paths = {
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"ABA": "C:\\Users\\user\\OneDrive - Universiti Sains Malaysia\\Assignment\\FYP\\l2arctic_release_v5.0\\ABA\\ABA\\wav",
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"BWC": "C:\\Users\\user\\OneDrive - Universiti Sains Malaysia\\Assignment\\FYP\\l2arctic_release_v5.0\\BWC\\BWC\\wav"
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}
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speaker_accent_map = {
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"ABA": "ar",
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"BWC": "zh"
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}
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splits = ["train", "test"]
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try:
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for speaker, accent in speaker_accent_map.items():
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audio_files = get_file_list_from_dir(audio_file_paths[speaker])
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shuffled_file_id = shuffle_file_id(audio_files)
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train, test = train_test_splits(shuffled_file_id, 0.7)
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print(train)
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print("Number of samples in training: ", len(train))
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print(test)
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print("Number of samples in testing: ", len(test))
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tar_file = tarfile.open(
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f"C:\\Users\\user\\OneDrive - Universiti Sains Malaysia\\Assignment\\FYP\\data\\audio\\{accent}\\train\\{accent}_train.tar",
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"w"
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)
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for id in train:
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# Copy training audio into a tar file
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tar_file.add(
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f"C:\\Users\\user\\OneDrive - Universiti Sains Malaysia\\Assignment\\FYP\\l2arctic_release_v5.0\\{speaker}\\{speaker}\\wav\\arctic_{id}.wav",
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arcname=f"arctic_{id}.wav"
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)
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# Copy training transcript into a tsv file
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with open(f"C:\\Users\\user\\OneDrive - Universiti Sains Malaysia\\Assignment\\FYP\\data\\transcript\\{accent}\\train.tsv", 'a') as tsv_file:
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with open(f"C:\\Users\\user\\OneDrive - Universiti Sains Malaysia\\Assignment\\FYP\\l2arctic_release_v5.0\\{speaker}\\{speaker}\\transcript\\arctic_{id}.txt") as txt_file:
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tsv_file.write(txt_file.read() + "\n")
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tsv_file.close()
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tar_file.close()
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# Copy testing audio into tar file
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tar_file = tarfile.open(
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f"C:\\Users\\user\\OneDrive - Universiti Sains Malaysia\\Assignment\\FYP\\data\\audio\\{accent}\\test\\{accent}_test.tar",
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"w"
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)
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for id in test:
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tar_file.add(
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f"C:\\Users\\user\\OneDrive - Universiti Sains Malaysia\\Assignment\\FYP\\l2arctic_release_v5.0\\{speaker}\\{speaker}\\wav\\arctic_{id}.wav",
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arcname=f"arctic_{id}.wav"
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)
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# Copy testing transcript into tsv file
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with open(f"C:\\Users\\user\\OneDrive - Universiti Sains Malaysia\\Assignment\\FYP\\data\\transcript\\{accent}\\test.tsv", 'a') as tsv_file:
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with open(f"C:\\Users\\user\\OneDrive - Universiti Sains Malaysia\\Assignment\\FYP\\l2arctic_release_v5.0\\{speaker}\\{speaker}\\transcript\\arctic_{id}.txt") as txt_file:
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tsv_file.write(txt_file.read() + "\n")
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tsv_file.close()
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tar_file.close()
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except PermissionError:
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print("Permission denied")
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release_stats.py
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STATS = {
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"name": "VerbaLex Voice",
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"version": "1.0.0",
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"accents": {
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"ar": {"numOfSpeaker": "1", "numOfWavFiles": "1129"},
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"zh": {"numOfSpeaker": "1", "numOfWavFiles": "1130"}
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}
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}
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verbalex_voice.py
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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 VerbaLex_Voice.accents import ACCENTS
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from VerbaLex_Voice.release_stats import STATS
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_HOMEPAGE = "https://huggingface.co/datasets/RitchieP/VerbaLex_voice"
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_LICENSE = "https://choosealicense.com/licenses/apache-2.0/"
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_BASE_URL = "https://huggingface.co/datasets/RitchieP/VerbaLex_voice/tree/main"
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_AUDIO_URL = _BASE_URL + "audio/{accent}/{split}/{accent}_{split}.tar"
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_TRANSCRIPT_URL = _BASE_URL + "transcript/{accent}/{split}.tsv"
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_CITATION = """\
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"""
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class VerbaLexVoiceConfig(datasets.BuilderConfig):
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def __init__(self, name, version, **kwargs):
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self.accent = kwargs.pop("accent", None)
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self.num_speakers = kwargs.pop("num_speakers", None)
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self.num_files = kwargs.pop("num_clips", None)
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description = (
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f"VerbaLex Voice english speech-to-text dataset in {self.accent} accent."
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)
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super(VerbaLexVoiceConfig, 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 VerbaLexVoiceDataset(datasets.GeneratorBasedBuilder):
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"""
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VerbaLex is a dataset containing different English accents from non-native English speakers.
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This dataset is created directly from the L2-Arctic dataset.
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"""
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BUILDER_CONFIGS = [
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VerbaLexVoiceConfig(
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name=accent,
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version=STATS["version"],
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accent=ACCENTS[accent],
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num_speakers=accent_stats["numOfSpeaker"],
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num_files=accent_stats["numOfWavFiles"]
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)
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for accent, accent_stats in STATS["accents"].items()
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]
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DEFAULT_CONFIG_NAME = "all"
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def _info(self):
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return datasets.DatasetInfo(
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description=(
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"VerbaLex Voice is a speech dataset focusing on accented English speech."
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"It specifically targets speeches from speakers that is a non-native English speaker."
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),
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features=datasets.Features(
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{
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"path": datasets.Value("string"),
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"accent": datasets.Value("string"),
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"sentence": datasets.Value("string"),
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"audio": datasets.Audio(sampling_rate=44_100)
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}
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),
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supervised_keys=None,
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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 _split_generators(self, dl_manager):
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"""Returns SplitGenerators"""
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accent = self.config.name
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splits = ("train", "test")
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audio_urls = {}
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for split in splits:
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audio_urls[split] = _AUDIO_URL.format(accent=accent, 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(accent=accent, split=split) for split in splits}
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meta_paths = dl_manager.download_and_extract(meta_urls)
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split_names = {
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"train": datasets.Split.TRAIN,
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"test": datasets.Split.TEST
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}
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split_generators = []
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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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"archives": [dl_manager.iter_archive(path) for path in 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, archives, 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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if not row["path"].endswith(".wav"):
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row["path"] += ".wav"
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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 i, audio_archive in enumerate(archives):
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for path, file in audio_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[i],
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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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