task_categories:automatic-speech-recognition size_categories:1K<n<10K language:ne asr nepali speech recognition nepali asr arxiv:2205.12446 region:us
---
dataset_info:
  features:
  - name: path
    dtype: string
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: transcription
    dtype: string
  splits:
  - name: train
    num_bytes: 4317304223.418399
    num_examples: 7091
  download_size: 5789483340
  dataset_size: 4317304223.418399
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- automatic-speech-recognition
language:
- ne
tags:
- asr
- nepali speech recognition
- nepali asr
pretty_name: Nepali Speech Dataset
size_categories:
- 1K<n<10K
---
# Dataset Card for "nepali-speech-dataset"

### Dataset Summary

The Nepali Speech Dataset is a collection of audio recordings and corresponding transcriptions in the Nepali language. It is designed to facilitate research and development in the field of speech recognition, natural language processing, and other related areas.

### Use Case

Speech recognition

### Languages

Nepali

## Dataset Creation

### Data Instances

{'path': '/home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/<br>7f8541f130925e9b2af7d37256f2f61f9d6ff21bf4a94f7c1a3803ec648d7d79/xs_chunks_0000/YOU0000000315_S0000660.wav',   
'audio':<br> {'path': '/home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/<br>7f8541f130925e9b2af7d37256f2f61f9d6ff21bf4a94f7c1a3803ec648d7d79/xs_chunks_0000/YOU0000000315_S0000660.wav',<br>
           'array': array([0.0005188 , 0.00085449, 0.00012207, ..., 0.00125122, 0.00076294,
       0.00036621], dtype=float32),<br>
           'sampling_rate': 16000<br>
           },<br>
'transcription': 'जुलियन बार्न्सद्वारा लिखित अङ्ग्रेजी उपन्यास'
 }

### Data Splits

Training Set: Around 6.7K of audio data with corresponding transcriptions

## Source

The dataset was created by extracting Nepali data points from *Common Voice*, *OpenSLR*, and *FLEURS* datasets.

**Common Voice**:

Common Voice is a Mozilla project that collects and shares multilingual, open-source, and crowdsourced voice data.

@inproceedings{commonvoice:2020,
  author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},<br>
  title = {Common Voice: A Massively-Multilingual Speech Corpus},<br>
  booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},<br>
  pages = {4211--4215},<br>
  year = 2020<br>
}

**OpenSLR**:

OpenSLR is a repository of open-source speech and language resources.

@inproceedings{kjartansson-etal-tts-sltu2018,
    title = {{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Framework for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}},<br>
    author = {Keshan Sodimana and Knot Pipatsrisawat and Linne Ha and Martin Jansche and Oddur Kjartansson and Pasindu De Silva and Supheakmungkol Sarin},<br>
    booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)},<br>
    year  = {2018},<br>
    address = {Gurugram, India},<br>
    month = aug,<br>
    pages = {66--70},<br>
    URL   = {https://dx.doi.org/10.21437/SLTU.2018-14}<br>
}

**FLEURS**:

FLEURS (Foreign Language Endangered Resources and Unicode Solutions) is a project that focuses on preserving and sharing linguistic resources for under-resourced languages.

@article{fleurs2022arxiv,
  title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech},<br>
  author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur},<br>
  journal={arXiv preprint arXiv:2205.12446},<br>
  url = {https://arxiv.org/abs/2205.12446},<br>
  year = {2022}<br>

**Nepali Speech to Text Dataset**:

 @misc{ishwor subedi_2023,
	title={Nepali Speech to Text Dataset},<br>
	url={https://www.kaggle.com/dsv/5806065},<br>
	DOI={10.34740/KAGGLE/DSV/5806065},<br>
	publisher={Kaggle},<br>
	author={Ishwor Subedi},<br>
	year={2023}<br>
}





