MASC.py

# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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""" MASC Dataset"""

# This script has been adopted from this dataset: "mozilla-foundation/common_voice_11_0"

import csv
import os
import json

import datasets
from datasets.utils.py_utils import size_str
from tqdm import tqdm

_CITATION = """\
@INPROCEEDINGS{10022652,
  author={Al-Fetyani, Mohammad and Al-Barham, Muhammad and Abandah, Gheith and Alsharkawi, Adham and Dawas, Maha},
  booktitle={2022 IEEE Spoken Language Technology Workshop (SLT)}, 
  title={MASC: Massive Arabic Speech Corpus}, 
  year={2023},
  volume={},
  number={},
  pages={1006-1013},
  doi={10.1109/SLT54892.2023.10022652}}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
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.
"""

_HOMEPAGE = "https://ieee-dataport.org/open-access/masc-massive-arabic-speech-corpus"
_LICENSE = "https://creativecommons.org/licenses/by/4.0/"
_BASE_URL = "https://huggingface.co/datasets/pain/MASC/resolve/main/"
_AUDIO_URL1 = _BASE_URL + "audio/{split}/{split}_{shard_idx}.tar.gz"
_AUDIO_URL2 = _BASE_URL + "audio/{split}/{split}_{shard_idx}.tar.xz"
_TRANSCRIPT_URL = _BASE_URL + "transcript/{split}/{split}.csv"

class MASC(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.0.0")

    def _info(self):

        features = datasets.Features(
            {
                "video_id": datasets.Value("string"),
                "start": datasets.Value("float64"),
                "end": datasets.Value("float64"),
                "duration": datasets.Value("float64"),
                "text": datasets.Value("string"),
                "type": datasets.Value("string"),
                "file_path": datasets.Value("string"),
                "audio": datasets.features.Audio(sampling_rate=16_000),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
            version=self.config.version,
        )

    def _split_generators(self, dl_manager):

        n_shards = {"train": 8,"dev": 1, "test": 1}
        audio_urls = {}
        splits = ("train", "dev", "test")

        for split in splits:
            audio_urls[split] = [
                _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]) 
            ]
        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(split=split) for split in splits}

        meta_paths = dl_manager.download(meta_urls)

        split_generators = []
        split_names = {
            "train": datasets.Split.TRAIN,
            "dev": datasets.Split.VALIDATION,
            "test": datasets.Split.TEST,
        }
        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=",", quoting=csv.QUOTE_NONE)
            for row in reader:
                if not row["file_path"].endswith(".wav"):
                    row["file_path"] += ".wav"
                for field in data_fields:
                    if field not in row:
                        row[field] = ""
                metadata[row["file_path"]] = row

        for i, audio_archive in enumerate(archives):
            for filename, file in audio_archive:
                _, filename = os.path.split(filename)
                if filename in metadata:
                    result = dict(metadata[filename])
                    # set the audio feature and the path to the extracted file
                    path = os.path.join(local_extracted_archive_paths[i], filename) if local_extracted_archive_paths else filename
                    
                    try:                        
                        result["audio"] = {"path": path, "bytes": file.read()}
                    except ReadError as e:
                        # Handle the ReadError
                        print("An error occurred while reading the data:", str(e))
                        continiue
                    # set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
                    result["file_path"] = path if local_extracted_archive_paths else filename
                    yield path, result