CoRover/OPEN-SLR-IISc-MILE

IISc-MILE Kannada ASR Corpus contains transcribed speech corpus for training ASR systems for Kannada language. It contains ~350 hours of read speech data collected from 915 speakers in a noise-free recording environment with high quality USB microphones.

The corpus is split as train and test and each folder contains two subfolders named audio_files and trans_files. The folder "audio_files" contains .wav file recordings (16 KhZ, 16 bit, mono, PCM format). The folder "trans_files" contains .txt files in UTF-8 Unicode text corresponding to each audio file.

This corpus is published by Medical Intelligence and Language Engineering Lab, Indian Institute of Science, Bangalore. The collection of this corpus is funded by Department of Kannada and Culture, Government of Karnataka.

Usage

from huggingface_hub import snapshot_download
local_dir = snapshot_download(
    repo_id="CoRover/OPEN-SLR-IISc-MILE",
    repo_type="dataset",
)

Citation

@misc{mile_1,
  doi = {10.48550/ARXIV.2207.13331},
  url = {https://arxiv.org/abs/2207.13331},
  author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A},
  title = {Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada},
  publisher = {arXiv},
  year = {2022},
}

@misc{mile_2,
  doi = {10.48550/ARXIV.2207.13333},
  url = {https://arxiv.org/abs/2207.13333},
  author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A},
  title = {Knowledge-driven Subword Grammar Modeling for Automatic Speech Recognition in Tamil and Kannada},
  publisher = {arXiv},
  year = {2022},
}

Acknowledgements

We acknowledge the Medical Intelligence and Language Engineering (MILE) Lab, Indian Institute of Science, Bangalore, for creating and releasing the corpus, the Department of Kannada and Culture, Government of Karnataka, for funding its collection, and OpenSLR for hosting it.

This repository is maintained by CoRover and provides selected Indic-language speech data for generative AI, speech recognition, and NLP research.

Disclaimer

CoRover does not claim ownership of the underlying recordings or transcripts, which remain the work of the original authors and are shared under CC BY 2.0. Users must give appropriate attribution and indicate if changes were made.

Users should independently evaluate the data for copyright, licensing, privacy, safety, quality, and other applicable requirements before using it for model training or downstream applications.