Create luxembourgish-asr-rtl-lu

luxembourgish-asr-rtl-lu

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+#!/usr/bin/env python3
+#
+#  Created by lemswasabi on 17/05/2022.
+#  Copyright © 2022 letzspeak. All rights reserved.
+#
+"""Luxembourgish ASR RTL.lu Dataset"""
+
+
+import os
+
+import datasets
+
+from datasets.tasks import AutomaticSpeechRecognition
+
+
+_DESCRIPTION = """\
+luxembourgish-asr-rtl-lu dataset is a speech corpus for the under-resourced Luxembourgish language.
+"""
+
+_URLS = {
+    "rtl-benchmark": "https://drive.google.com/uc?id=1IiFV6TZHH1sOBL409VnmxCXSSyQkue0F&export=download&confirm=t",
+}
+
+class Tuudle(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="rtl-benchmark", version=VERSION, description="This part contains benchmark of samples collected from the RTL.lu domain"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "tuudle"
+
+    def _info(self):
+
+        features = datasets.Features(
+            {
+                "audio": datasets.Audio(sampling_rate=16_000),
+                "sentence": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=("audio", "sentence"),
+            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="sentence")],
+        )
+
+    def _split_generators(self, dl_manager):
+
+        urls = _URLS[self.config.name]
+        archive_path  = dl_manager.download_and_extract(urls)
+        metadata_filepaths = {
+            split: os.path.join(archive_path, os.path.join(split, f"{split}.tsv"))
+            for split in ["train", "test", "dev"]
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "local_extracted_archive": archive_path,
+                    "metadata_filepath": metadata_filepaths["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "local_extracted_archive": archive_path,
+                    "metadata_filepath": metadata_filepaths["test"],
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "local_extracted_archive": archive_path,
+                    "metadata_filepath": metadata_filepaths["dev"],
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, local_extracted_archive, metadata_filepath, split):
+
+        path_to_clips  = os.path.join(local_extracted_archive, split)
+
+        with open(metadata_filepath, encoding="utf-8") as f:
+            lines = f.readlines()
+            for key, line in enumerate(lines[1:]):
+                field_values = line.strip().split("\t")
+                if len(field_values) == 2:
+                    audio_filename, sentence = field_values[0], field_values[1]
+                    audio_path = os.path.join(path_to_clips, audio_filename)
+                    yield key, {
+                        "audio": {"path": audio_path, "bytes": open(audio_path, "rb").read()},
+                        "sentence": sentence,
+                    }