Update README.md

README.md

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     num_bytes: 5844021677.902
     num_examples: 7481
   - name: test
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     num_examples: 726
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     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.