mgb2_speech.py

import datasets
import os


_DESCRIPTION = "MGB2 speech recognition dataset AR"
_HOMEPAGE = "https://arabicspeech.org/mgb2/"
_LICENSE = "MGB-2 License agreement"
_CITATION = """@misc{https://doi.org/10.48550/arxiv.1609.05625,
  doi = {10.48550/ARXIV.1609.05625},
  
  url = {https://arxiv.org/abs/1609.05625},
  
  author = {Ali, Ahmed and Bell, Peter and Glass, James and Messaoui, Yacine and Mubarak, Hamdy and Renals, Steve and Zhang, Yifan},
  
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition},
  
  publisher = {arXiv},
  
  year = {2016},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}
"""
_DATA_ARCHIVE_ROOT = "archives/"
_DATA_URL = {
    "test": _DATA_ARCHIVE_ROOT + "mgb2_wav.test.tar.gz",
    "dev": _DATA_ARCHIVE_ROOT + "mgb2_wav.dev.tar.gz",
    "train": [_DATA_ARCHIVE_ROOT + f"mgb2_wav_{x}.train.tar.gz" for x in range(48)], # we have 48 archives
}
_TEXT_URL = {
    "test": _DATA_ARCHIVE_ROOT + "mgb2_txt.test.tar.gz",
    "dev": _DATA_ARCHIVE_ROOT + "mgb2_txt.dev.tar.gz",
    "train": _DATA_ARCHIVE_ROOT + "mgb2_txt.train.tar.gz",
}

class MGDB2Dataset(datasets.GeneratorBasedBuilder):
    def _info(self):
        return datasets.DatasetInfo(
        description=_DESCRIPTION,
        features=datasets.Features(
            {
                "path": datasets.Value("string"),
                "audio": datasets.Audio(sampling_rate=16_000),
                "text": datasets.Value("string"),
            }
        ),
        supervised_keys=None,
        homepage=_HOMEPAGE,
        license=_LICENSE,
        citation=_CITATION,
    )

    def _split_generators(self, dl_manager):
        wav_archive = dl_manager.download(_DATA_URL)
        txt_archive = dl_manager.download(_TEXT_URL)
        test_dir = "dataset/test"
        dev_dir = "dataset/dev"
        train_dir = "dataset/train"

        if dl_manager.is_streaming:
    
            return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "path_to_txt": test_dir + "/txt",
                    "path_to_wav": test_dir + "/wav",
                    "wav_files": [dl_manager.iter_archive(wav_archive['test'])],
                    "txt_files": dl_manager.iter_archive(txt_archive['test']),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "path_to_txt": dev_dir + "/txt",
                    "path_to_wav": dev_dir + "/wav",
                    "wav_files": [dl_manager.iter_archive(wav_archive['dev'])],
                    "txt_files": dl_manager.iter_archive(txt_archive['dev']),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "path_to_txt": train_dir + "/txt",
                    "path_to_wav": train_dir + "/wav",
                    "wav_files": [dl_manager.iter_archive(a) for a in wav_archive['train']],
                    "txt_files": dl_manager.iter_archive(txt_archive['train']),
                },
            ),
        ]
        else:
            return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "path_to_txt": test_dir + "/txt",
                    "path_to_wav": test_dir + "/wav",
                    "wav_files": [dl_manager.extract(wav_archive['test'])],
                    "txt_files": dl_manager.extract(txt_archive['test']),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "path_to_txt": dev_dir + "/txt",
                    "path_to_wav": dev_dir + "/wav",
                    "wav_files": [dl_manager.extract(wav_archive['dev'])],
                    "txt_files": dl_manager.extract(txt_archive['dev']),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "path_to_txt": train_dir + "/txt",
                    "path_to_wav": train_dir + "/wav",
                    "wav_files": [dl_manager.extract(a) for a in wav_archive['train']],
                    "txt_files": dl_manager.extract(txt_archive['train']),
                },
            ),
        ]


    
    def _generate_examples(self, path_to_txt, path_to_wav, wav_files, txt_files):
        """ 
        This assumes that the text directory alphabetically precedes the wav dir
        The file names for wav and text seem to match and are unique
        We can use them for the dictionary matching them
        """
        examples = {}
        id_ = 0
        # need to prepare the transcript - wave map
        for path, f in txt_files:
            if path.find(path_to_txt) > -1:

                wav_path = os.path.split(path)[1].replace("_utf8", "").replace(".txt", ".wav").strip()

                txt = f.read().decode(encoding="utf-8").strip()
                examples[wav_path] = {
                    "text": txt,
                    "path": wav_path,
                }

        for wf in wav_files:
            for path, f in wf:
                if path.find(path_to_wav) > -1:
                    wav_path = os.path.split(path)[1].strip()
                    audio = {"path": path, "bytes": f.read()}
                    yield id_, {**examples[wav_path], "audio": audio}
                    id_ += 1