---
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: transcription
    dtype: string
  splits:
  - name: train
    num_bytes: 5844021677.902
    num_examples: 7481
  - name: test
    num_bytes: 526633107
    num_examples: 726
  download_size: 5452408390
  dataset_size: 6370654784.902
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: mit
task_categories:
- automatic-speech-recognition
language:
- ne
---

# Nepali Speech-to-Text Dataset  

This repository contains a dataset for **Automatic Speech Recognition (ASR)** in the **Nepali language**. The dataset is designed for supervised learning tasks and includes **audio files along with their corresponding transcriptions**. The audio samples have been **collected from various open-source platforms and other publicly available sources** on the internet.  

Each audio file has an average length of **15 seconds** and has been **converted into a consistent WAV format** for ease of processing.  

## Dataset Structure  

The dataset is **split into training and testing sets**:  
- **Training Data:** Contains a diverse set of Nepali speech samples from multiple sources.  
- **Testing Data:** Includes the **Fleurs test data for Nepali** to ensure evaluation consistency.  

## Audio Data Overview  

The total dataset contains **approximately 22.87 hours** of audio. Below is the breakdown of the dataset:  

### Table 1: Audio data length from different sources  

| **Dataset**                 | **Audio size (Hrs)** |
|-----------------------------|----------------------|
| Common Voice 20             | 1.71                 |
| Google Fleurs               | 14.38                |
| OpenSLR 143                 | 1.24                 |
| OpenSLR 43                  | 2.80                 |
| Nepali Parliament Audio     | 2.74                 |
| **Total**                   | **22.87**            |

The dataset includes **high variability** in terms of **speakers (age groups, genders), noisy environments, different dialects, and various acoustic conditions**, making it robust for ASR training.  

## Important Notes  

- The dataset is in **raw form**, meaning **preprocessing and other corrections may be required** before training an ASR model.  
- The transcriptions have been obtained from open datasets and may contain **errors or inconsistencies** that need to be addressed during data preparation.  

## References  

- **Conneau, A., Ma, M., Khanuja, S., Zhang, Y., Axelrod, V., Dalmia, S., Riesa, J., Rivera, C., & Bapna, A. (2022).**  
  *FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech.*  
  arXiv preprint arXiv:2205.12446. [Link](https://arxiv.org/abs/2205.12446)  

- **Sodimana, K., Pipatsrisawat, K., Ha, L., Jansche, M., Kjartansson, O., De Silva, P., & Sarin, S. (2018).**  
  *A Step-by-Step Process for Building TTS Voices Using Open Source Data and Framework for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese.*  
  Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU), Gurugram, India, pp. 66-70.  
  [DOI Link](http://dx.doi.org/10.21437/SLTU.2018-14)  

- **Khadka, S., G.C., R., Paudel, P., Shah, R., & Joshi, B. (2023).**  
  *Nepali Text-to-Speech Synthesis using Tacotron2 for Melspectrogram Generation.*  
  SIGUL 2023, 2nd Annual Meeting of the Special Interest Group on Under-resourced Languages: a Satellite Workshop of Interspeech 2023.  

- **Ardila, R., Branson, M., Davis, K., Henretty, M., Kohler, M., Meyer, J., Morais, R., Saunders, L., Tyers, F. M., & Weber, G. (2020).**  
  *Common Voice: A Massively-Multilingual Speech Corpus.*  
  Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pp. 4211-4215.