Datasets:
Tasks:
Automatic Speech Recognition
Languages:
Vietnamese
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
# coding=utf-8 | |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import datasets | |
_CITATION = """\ | |
@inproceedings{luong-vu-2016-non, | |
title = "A non-expert {K}aldi recipe for {V}ietnamese Speech Recognition System", | |
author = "Luong, Hieu-Thi and | |
Vu, Hai-Quan", | |
booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)", | |
month = dec, | |
year = "2016", | |
address = "Osaka, Japan", | |
publisher = "The COLING 2016 Organizing Committee", | |
url = "https://aclanthology.org/W16-5207", | |
pages = "51--55", | |
} | |
""" | |
_DESCRIPTION = """\ | |
VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for | |
Vietnamese Automatic Speech Recognition task. | |
The corpus was prepared by AILAB, a computer science lab of VNUHCM - University of Science, with Prof. Vu Hai Quan is the head of. | |
We publish this corpus in hope to attract more scientists to solve Vietnamese speech recognition problems. | |
""" | |
_HOMEPAGE = "https://doi.org/10.5281/zenodo.7068130" | |
_LICENSE = "CC BY-NC-SA 4.0" | |
# Source data: "https://zenodo.org/record/7068130/files/vivos.tar.gz" | |
_DATA_URL = "data/vivos.tar.gz" | |
_PROMPTS_URLS = { | |
"train": "data/prompts-train.txt.gz", | |
"test": "data/prompts-test.txt.gz", | |
} | |
class VivosDataset(datasets.GeneratorBasedBuilder): | |
"""VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for | |
Vietnamese Automatic Speech Recognition task.""" | |
VERSION = datasets.Version("1.1.0") | |
# This is an example of a dataset with multiple configurations. | |
# If you don't want/need to define several sub-sets in your dataset, | |
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. | |
# If you need to make complex sub-parts in the datasets with configurable options | |
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
# BUILDER_CONFIG_CLASS = MyBuilderConfig | |
def _info(self): | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"speaker_id": datasets.Value("string"), | |
"path": datasets.Value("string"), | |
"audio": datasets.Audio(sampling_rate=16_000), | |
"sentence": datasets.Value("string"), | |
} | |
), | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name | |
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs | |
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
prompts_paths = dl_manager.download_and_extract(_PROMPTS_URLS) | |
archive = dl_manager.download(_DATA_URL) | |
train_dir = "vivos/train" | |
test_dir = "vivos/test" | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"prompts_path": prompts_paths["train"], | |
"path_to_clips": train_dir + "/waves", | |
"audio_files": dl_manager.iter_archive(archive), | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"prompts_path": prompts_paths["test"], | |
"path_to_clips": test_dir + "/waves", | |
"audio_files": dl_manager.iter_archive(archive), | |
}, | |
), | |
] | |
def _generate_examples(self, prompts_path, path_to_clips, audio_files): | |
"""Yields examples as (key, example) tuples.""" | |
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. | |
# The `key` is here for legacy reason (tfds) and is not important in itself. | |
examples = {} | |
with open(prompts_path, encoding="utf-8") as f: | |
for row in f: | |
data = row.strip().split(" ", 1) | |
speaker_id = data[0].split("_")[0] | |
audio_path = "/".join([path_to_clips, speaker_id, data[0] + ".wav"]) | |
examples[audio_path] = { | |
"speaker_id": speaker_id, | |
"path": audio_path, | |
"sentence": data[1], | |
} | |
inside_clips_dir = False | |
id_ = 0 | |
for path, f in audio_files: | |
if path.startswith(path_to_clips): | |
inside_clips_dir = True | |
if path in examples: | |
audio = {"path": path, "bytes": f.read()} | |
yield id_, {**examples[path], "audio": audio} | |
id_ += 1 | |
elif inside_clips_dir: | |
break | |