Create dataset

test.tsv

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+audio_id	ngram
+common_voice_pl_20547774.wav	poślemy
+common_voice_pl_20547775.wav	poślemy po
+common_voice_pl_20547776.wav	poślemy po was

test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8bf58199eb644adf8de5caceea10dfdd7a52e45de539d73bcbf669c616f897aa
+size 48704

test2.py

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+# 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