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Datasets:
linhtran92
/
viet_bud500

Tasks:
Automatic Speech Recognition
Modalities:
Audio
Text
Formats:
parquet
Languages:
Vietnamese
Size:
100K - 1M
Libraries:
Datasets
Dask
License:
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Dataset Summary

Introducing Bud500, a comprehensive Vietnamese speech corpus designed to support ASR research community. With aprroximately 500 hours of audio, it covers a broad spectrum of topics including podcast, travel, book, food, and so on, while spanning accents from Vietnam's North, South, and Central regions. Derived from free public audio resources, this publicly accessible dataset is designed to significantly enhance the work of developers and researchers in the field of speech recognition.

The corpus was prepared by VietAI research team, a non-profit organization with the mission of nurturing AI talents and building a community of world-class AI experts in Vietnam.

Languages

Vietnamese

Dataset Structure

A typical data point comprises the Audio object dict audio and its transcription.

{'audio': {'path': None,
           'array': array([0.00125122, 0.00228882, 0.00213623, ..., 0.00354004, 0.00442505, 0.00650024]),
           'sampling_rate': 16000},
 'transcription': 'ai cho phép em uống nhiều rượu như vậy'}

Data Fields

  • audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: dataset[0]["audio"] the audio file is automatically decoded and resampled to dataset.features["audio"].sampling_rate. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the "audio" column, i.e. dataset[0]["audio"] should always be preferred over dataset["audio"][0].

  • transcription: textual form of the audio content.

Data Splits

The speech material has been subdivided into portions for train, test and validation.

Total size: 98Gb Train Validation Test
Samples 634158 7500 7500
Duration ~500h ~5.46h ~5.46h

Example usage

Open In Colab

Datasets

from huggingface_hub import notebook_login
from datasets import load_dataset
notebook_login()
auth_token="your huggingface token"

# load from parquet file (~4000 samples in a parquet file)
# link to other parquet files: https://huggingface.co./datasets/linhtran92/viet_bud500/tree/main/data
train_url = "https://huggingface.co./datasets/linhtran92/viet_bud500/resolve/main/data/train-00000-of-00105-be5f872f8be772f5.parquet"
test_url = "https://huggingface.co./datasets/linhtran92/viet_bud500/resolve/main/data/test-00000-of-00002-531c1d81edb57297.parquet"
data_files = {"train": train_url, "test" : test_url}
dataset = load_dataset("parquet", data_files=data_files, use_auth_token=auth_token)

# load all (649158 samples)
dataset = load_dataset("linhtran92/viet_bud500", split="test", use_auth_token=auth_token)

Dataset Creation

  • Source Data

  • Considerations for Using the Data

  • Social Impact of Dataset

  • Discussion of Biases

Other Known Limitations

  • Dataset provided for research purposes only. Please check dataset license for additional information.

Dataset Curators

  • The dataset was initially prepared by VietAI research team, a non-profit organization with the mission of nurturing AI talents and building a community of world-class AI experts in Vietnam.

Additional Information

License 

Copyright (c) 2024 VietAI Research

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.

Citation Information 

@misc{Bud500,
  author = {Anh Pham, Khanh Linh Tran, Linh Nguyen, Thanh Duy Cao, Phuc Phan},
  title = {{Bud500},
  url = {https://github.com/quocanh34/Bud500},
  year = {2024}
}

Contributions

Thanks to @quocanh34 @linhtran6065 @linhqyy @thanhduycao @pphuc25 for making this dataset possible.

Please CITE our repo when it is used to help produce published results or is incorporated into other software.

Contact

[email protected]