Datasets:
Tasks:
Automatic Speech Recognition
Formats:
parquet
Languages:
Nepali
Size:
1K - 10K
ArXiv:
License:
File size: 3,828 Bytes
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---
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.
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