test2.py

# coding=utf-8
# Lint as: python3
"""test set"""


import csv
import os
import json

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


_CITATION = """\
@inproceedings{panayotov2015librispeech,
  title={Librispeech: an ASR corpus based on public domain audio books},
  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
  pages={5206--5210},
  year={2015},
  organization={IEEE}
}
"""

_DESCRIPTION = """\
Lorem ipsum
"""


_BASE_URL = "https://huggingface.co/datasets/j-krzywdziak/test/tree/main"
_DATA_URL = "test.zip"
_PROMPTS_URLS = {"test": "test.tsv"}

logger = datasets.logging.get_logger(__name__)

class TestConfig(datasets.BuilderConfig):
    """Lorem impsum."""

    def __init__(self, name, **kwargs):
        # self.language = kwargs.pop("language", None)
        # self.release_date = kwargs.pop("release_date", None)
        # self.num_clips = kwargs.pop("num_clips", None)
        # self.num_speakers = kwargs.pop("num_speakers", None)
        # self.validated_hr = kwargs.pop("validated_hr", None)
        # self.total_hr = kwargs.pop("total_hr", None)
        # self.size_bytes = kwargs.pop("size_bytes", None)
        # self.size_human = size_str(self.size_bytes)
        description = (
            f"Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor "
            f"incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud "
            f"exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure "
            f"dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. "
            f"Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt "
            f"mollit anim id est laborum."
        )
        super(TestConfig, self).__init__(
            name=name,
            description=description,
            **kwargs,
        )

class TestASR(datasets.GeneratorBasedBuilder):
    """Lorem ipsum."""


    BUILDER_CONFIGS = [
        TestConfig(
            name="Test Dataset",
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "audio_id": datasets.Value("string"),
                    "audio": datasets.Audio(sampling_rate=16_000),
                    "ngram": datasets.Value("string")
                }
            ),
            supervised_keys=None,
            homepage=_BASE_URL,
            citation=_CITATION
        )

    def _split_generators(self, dl_manager):
        # audio_path = dl_manager.download(_DATA_URL)
        # local_extracted_archive = dl_manager.extract(audio_path) if not dl_manager.is_streaming else None
        meta_path = dl_manager.download(_PROMPTS_URLS)
        audio_path = dl_manager.download_and_extract(_DATA_URL)
        return [datasets.SplitGenerator(
            name=datasets.Split.TEST,
            gen_kwargs={
                "meta_path": meta_path["test"],
                "audio_files": dl_manager.iter_files(audio_path),
                # "local_extracted_archive": local_extracted_archive,
            }
        )]

    def _generate_examples(self, meta_path, audio_files):#, local_extracted_archive):
        """Lorem ipsum."""
        data_fields = list(self._info().features.keys())
        metadata = {}
        with open(meta_path, encoding="utf-8") as f:
            next(f)
            for row in f:
                print(row)
                r = row.split("\t")
                print(r)
                audio_id = r[0]
                ngram = r[1]
                metadata[audio_id] = {"audio_id": audio_id,
                                      "ngram": ngram}

        id_ = 0
        for path in audio_files:
            import pdb;pdb.set_trace()
            print(path, f)
            *_, audio_name = os.path.split(path)
            if audio_name in metadata:
                result = dict(metadata[audio_name])
                result["audio"] = path#{"path": path, "bytes":f.read()}
                yield id_, result
                id_ +=1