Datasets:
Languages:
Polish
Multilinguality:
monolingual
Annotations Creators:
expert-generated
Tags:
License:
| # 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 = "https://huggingface.co/datasets/j-krzywdziak/test/resolve/main/test.zip" | |
| _PROMPTS_URLS = {"test": "https://huggingface.co/datasets/j-krzywdziak/test/raw/main/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="TestDataset", | |
| ) | |
| ] | |
| 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) | |
| return [datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "meta_path": meta_path["test"], | |
| "audio_files": dl_manager.iter_archive(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, f in audio_files: | |
| print(path, f) | |
| _, audio_name = os.path.split(path) | |
| if audio_name in metadata: | |
| result = dict(metadata[audio_name]) | |
| path = os.path.join(path, 'test') | |
| result["audio"] = {"path": path, "bytes":f.read()} | |
| yield id_, result | |
| id_ +=1 | |