Create MASC.py
Browse files
MASC.py
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# coding=utf-8
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# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" MASC Dataset"""
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# This script has been adopted from this dataset: "mozilla-foundation/common_voice_11_0"
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import csv
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import os
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import json
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import datasets
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from datasets.utils.py_utils import size_str
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from tqdm import tqdm
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_CITATION = """\
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@INPROCEEDINGS{10022652,
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author={Al-Fetyani, Mohammad and Al-Barham, Muhammad and Abandah, Gheith and Alsharkawi, Adham and Dawas, Maha},
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booktitle={2022 IEEE Spoken Language Technology Workshop (SLT)},
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title={MASC: Massive Arabic Speech Corpus},
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year={2023},
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volume={},
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number={},
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pages={1006-1013},
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doi={10.1109/SLT54892.2023.10022652}}
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}
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"""
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# TODO: Add description of the dataset here
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# You can copy an official description
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_DESCRIPTION = """\
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MASC is a dataset that contains 1,000 hours of speech sampled at 16 kHz and crawled from over 700 YouTube channels. The dataset is multi-regional, multi-genre, and multi-dialect intended to advance the research and development of Arabic speech technology with a special emphasis on Arabic speech recognition.
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"""
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_HOMEPAGE = "https://ieee-dataport.org/open-access/masc-massive-arabic-speech-corpus"
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_LICENSE = "https://creativecommons.org/licenses/by/4.0/"
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_BASE_URL = "https://huggingface.co/datasets/pain/MASC/resolve/main/"
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_AUDIO_URL1 = _BASE_URL + "audio/{split}/{split}_{shard_idx}.tar.gz"
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_AUDIO_URL2 = _BASE_URL + "audio/{split}/{split}_{shard_idx}.tar.xz"
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_TRANSCRIPT_URL = _BASE_URL + "transcript/{split}/{split}.csv"
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class MASC(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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def _info(self):
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features = datasets.Features(
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{
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"video_id": datasets.Value("string"),
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"start": datasets.Value("float64"),
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"end": datasets.Value("float64"),
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"duration": datasets.Value("float64"),
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"text": datasets.Value("string"),
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"type": datasets.Value("string"),
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"file_path": datasets.Value("string"),
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"audio": datasets.features.Audio(sampling_rate=16_000),
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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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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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version=self.config.version,
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)
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def _split_generators(self, dl_manager):
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n_shards = {"train": 8,"dev": 1, "test": 1}
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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] = [
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_AUDIO_URL2.format(split=split, shard_idx="{:02d}".format(i+1)) if split=="train" else _AUDIO_URL1.format(split=split, shard_idx="{:02d}".format(i+1)) for i in range(n_shards[split])
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]
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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(split=split) for split in splits}
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meta_paths = dl_manager.download(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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"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=",", quoting=csv.QUOTE_NONE)
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for row in reader:
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if not row["file_path"].endswith(".wav"):
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row["file_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["file_path"]] = row
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for i, audio_archive in enumerate(archives):
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for filename, file in audio_archive:
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_, filename = os.path.split(filename)
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if filename in metadata:
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result = dict(metadata[filename])
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# set the audio feature and the path to the extracted file
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path = os.path.join(local_extracted_archive_paths[i], filename) if local_extracted_archive_paths else filename
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try:
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result["audio"] = {"path": path, "bytes": file.read()}
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except ReadError as e:
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# Handle the ReadError
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print("An error occurred while reading the data:", str(e))
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continiue
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# set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
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result["file_path"] = path if local_extracted_archive_paths else filename
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yield path, result
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