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README.md

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+---
+pretty_name: Rakeffet
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+language:
+- en
+license:
+- cc-by-nc-4.0
+multilinguality:
+- monolingual
+source_datasets:
+- original
+task_categories:
+- automatic-speech-recognition
+- audio-classification
+- speech-synthesis
+---
+
+# Dataset Card for Rakeffet
+
+
+
+

dataset_infos.json

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+{  
+    "description": "rakeffet",    
+    "homepage": "google.com",    
+    "supervised_keys": {
+      "input": "id",
+      "output": "text"
+    },
+    "task_templates": [
+      {
+        "task": "automatic-speech-recognition",
+        "audio_column": "audio",
+        "transcription_column": "text"
+      }
+    ],
+    "builder_name": "rakeffet",
+    "version": {
+      "version_str": "1.0.0",
+      "description": "",
+      "major": 2,
+      "minor": 1,
+      "patch": 0
+    }
+}

rakeffet.py

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+"""Rakeffet dataset."""
+
+
+import os
+
+import datasets
+from datasets import load_dataset
+from datasets.tasks import AutomaticSpeechRecognition
+
+
+_CITATION = """\
+@inproceedings{Zandie2021RakeffetAC,
+  title={Rakeffet AI},
+  author={Yisroel Lazerson},
+  booktitle={Cooolio},
+  year={2022}
+}
+"""
+
+_DESCRIPTION = "Rakeffet is cool."
+_URL = "google.com"
+_NAME = "rakeffet"
+
+_DL_URLS = {
+    "dev": "https://huggingface.co/datasets/izzy-lazerson/rakeffet/resolve/main/data/dev.tar.gz",
+    "test": "https://huggingface.co/datasets/izzy-lazerson/rakeffet/resolve/main/data/test.tar.gz",
+    "train": "https://huggingface.co/datasets/izzy-lazerson/rakeffet/resolve/main/data/train.tar.gz"
+}
+
+
+class RakeffetConfig(datasets.BuilderConfig):
+    """BuilderConfig for Rakeffet."""
+
+    def __init__(self, **kwargs):
+        """
+        Args:
+          data_dir: `string`, the path to the folder containing the files in the
+            downloaded .tar
+          citation: `string`, citation for the data set
+          url: `string`, url for information about the data set
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(
+            RakeffetConfig,
+            self
+        ).__init__(
+            version=datasets.Version("1.0.0", ""),
+            **kwargs
+        )
+
+
+class Rakeffet(datasets.GeneratorBasedBuilder):
+    """Rakeffet dataset."""
+
+    BUILDER_CONFIGS = [
+        RakeffetConfig(
+            name=_NAME,
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "text": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=("id", "text"),
+            homepage=_URL,
+            citation=_CITATION,
+            task_templates=[
+                AutomaticSpeechRecognition(
+                    audio_column="audio",
+                    transcription_column="text"
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download(_DL_URLS)
+        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
+        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
+
+        train_splits = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "local_extracted_archive": local_extracted_archive.get("train"),
+                    "files": dl_manager.iter_archive(archive_path["train"]),
+                },
+            )
+        ]
+        dev_splits = [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "local_extracted_archive": local_extracted_archive.get("dev"),
+                    "files": dl_manager.iter_archive(archive_path["dev"]),
+                },
+            )
+        ]
+        test_splits = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "local_extracted_archive": local_extracted_archive.get("test"),
+                    "files": dl_manager.iter_archive(archive_path["test"]),
+                },
+            )
+        ]
+
+        return train_splits + dev_splits + test_splits
+
+    def _generate_examples(self, files, local_extracted_archive):
+        """Generate examples from a Rakeffet archive_path."""
+        audio_data = {}
+        transcripts = {}
+        paths = {}
+        for path, f in files:
+            if path.endswith(".mp3"):
+                id_ = path.split("/")[-1][: -len(".mp3")]
+                audio_data[id_] = f.read()
+                paths[id_] = os.path.join(local_extracted_archive, path)
+            elif path.endswith(".csv"):
+                for line in f:
+                    line_fields = line.decode("utf-8").split(',')
+                    id_ = line_fields[0]
+                    transcripts[id_] = line_fields[1].strip()
+
+        for key, id_ in enumerate(transcripts):
+            yield key, {"audio": {"bytes": audio_data[id_],
+                                  "path": paths[id_]},
+                        "text": transcripts[id_],
+                        "id": id_}