deleted script

dataviewer-test.py

@@ -1,129 +0,0 @@
-# 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
-from datasets.tasks import AutomaticSpeechRecognition
-
-
-_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/polinaeterna/test-user"
-_DATA_URL = "data/test.zip"
-_PROMPTS_URLS = {"test": "transcript/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="dataviewer-test",
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "audio_id": datasets.Value("string"),
-                    "audio": datasets.Audio(),
-                    "ngram": datasets.Value("string")
-                }
-            ),
-            supervised_keys=None,
-            homepage=_BASE_URL,
-            citation=_CITATION,
-            task_templates=[
-                AutomaticSpeechRecognition(transcription_column="ngram")
-            ],
-        )
-
-    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(local_extracted_archive, "test", path) if local_extracted_archive else path
-                result["audio"] = {"path": path, "bytes":f.read()}
-                yield id_, result
-                id_ +=1