Datasets:
Languages:
Luxembourgish
License:
File size: 3,347 Bytes
903d3e2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
#!/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,
}
|