task_categories:automatic-speech-recognition annotations_creators:expert-generated language_creators:crowdsourced language_creators:expert-generated multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:vi license:cc-by-nc-sa-4.0 region:us
\
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.
\
@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",
}
---
pretty_name: VIVOS
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- vi
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
dataset_info:
  features:
  - name: speaker_id
    dtype: string
  - name: path
    dtype: string
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: sentence
    dtype: string
  splits:
  - name: train
    num_bytes: 1722002133
    num_examples: 11660
  - name: test
    num_bytes: 86120227
    num_examples: 760
  download_size: 1475540500
  dataset_size: 1808122360
---

# Dataset Card for VIVOS

## Table of Contents
- [Dataset Card for VIVOS](#dataset-card-for-vivos)
  - [Table of Contents](#table-of-contents)
  - [Dataset Description](#dataset-description)
    - [Dataset Summary](#dataset-summary)
    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
    - [Languages](#languages)
  - [Dataset Structure](#dataset-structure)
    - [Data Instances](#data-instances)
    - [Data Fields](#data-fields)
    - [Data Splits](#data-splits)
  - [Dataset Creation](#dataset-creation)
    - [Curation Rationale](#curation-rationale)
    - [Source Data](#source-data)
      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
      - [Who are the source language producers?](#who-are-the-source-language-producers)
    - [Annotations](#annotations)
      - [Annotation process](#annotation-process)
      - [Who are the annotators?](#who-are-the-annotators)
    - [Personal and Sensitive Information](#personal-and-sensitive-information)
  - [Considerations for Using the Data](#considerations-for-using-the-data)
    - [Social Impact of Dataset](#social-impact-of-dataset)
    - [Discussion of Biases](#discussion-of-biases)
    - [Other Known Limitations](#other-known-limitations)
  - [Additional Information](#additional-information)
    - [Dataset Curators](#dataset-curators)
    - [Licensing Information](#licensing-information)
    - [Citation Information](#citation-information)
    - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://doi.org/10.5281/zenodo.7068130
- **Repository:** [Needs More Information]
- **Paper:** [A non-expert Kaldi recipe for Vietnamese Speech Recognition System](https://aclanthology.org/W16-5207/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [AILAB](mailto:ailab@hcmus.edu.vn)

### Dataset Summary

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.

### Supported Tasks and Leaderboards

[Needs More Information]

### Languages

Vietnamese

## Dataset Structure

### Data Instances

A typical data point comprises the path to the audio file, called `path` and its transcription, called `sentence`. Some additional information about the speaker and the passage which contains the transcription is provided.

```
{'speaker_id': 'VIVOSSPK01',
 'path': '/home/admin/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/vivos/train/waves/VIVOSSPK01/VIVOSSPK01_R001.wav',
 'audio': {'path': '/home/admin/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/vivos/train/waves/VIVOSSPK01/VIVOSSPK01_R001.wav',
		   'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32),
		   'sampling_rate': 16000},
 'sentence': 'KHÁCH SẠN'}
```

### Data Fields

- speaker_id: An id for which speaker (voice) made the recording

- path: The path to the audio file

- 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]`.

- sentence: The sentence the user was prompted to speak

### Data Splits

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

Speech was recorded in a quiet environment with high quality microphone, speakers were asked to read one sentence at a time.

|                  | Train | Test  |
| ---------------- | ----- | ----- |
| Speakers         | 46    | 19    | 
| Utterances       | 11660 | 760   |
| Duration         | 14:55 | 00:45 |
| Unique Syllables | 4617  | 1692  |

## Dataset Creation

### Curation Rationale

[Needs More Information]

### Source Data

#### Initial Data Collection and Normalization

[Needs More Information]

#### Who are the source language producers?

[Needs More Information]

### Annotations

#### Annotation process

[Needs More Information]

#### Who are the annotators?

[Needs More Information]

### Personal and Sensitive Information

The dataset consists of people who have donated their voice online.  You agree to not attempt to determine the identity of speakers in this dataset.

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed] 

### Other Known Limitations

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

## Additional Information

### Dataset Curators

The dataset was initially prepared by AILAB, a computer science lab of VNUHCM - University of Science.

### Licensing Information

Public Domain, Creative Commons Attribution NonCommercial ShareAlike v4.0 ([CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode))

### Citation Information

```
@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",
}
```

### Contributions

Thanks to [@binh234](https://github.com/binh234) for adding this dataset.