Datasets:
Languages:
Luxembourgish
License:
| #!/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, | |
| } | |