README.md

metadata

pretty_name: Snow Mountain
language:
  - hi
  - bgc
  - kfs
  - dgo
  - bhd
  - gbk
  - xnr
  - kfx
  - mjl
  - kfo
  - bfz
  - mal
annotations_creators:
  - 'null': null
language_creators:
  - 'null': null
license: []
multilinguality:
  - multilingual
size_categories:
  - null
source_datasets:
  - Snow Mountain
tags: []
task_categories:
  - automatic-speech-recognition
task_ids: []
configs:
  - mal
dataset_info:
  - config_name: mal
    features:
      - name: Unnamed
        dtype: int64
      - name: sentence
        dtype: string
      - name: path
        dtype: string
    splits:
      - name: train_500
        num_bytes: 1295622
        num_examples: 22
      - name: test_common
        num_bytes: 411844
        num_examples: 9
      - name: val_500
      - name: train_1000
        num_bytes: 40023390
        num_examples: 752
      - name: val_1000
        num_bytes: 1707426
        num_examples: 31
    download_size: 41038412
    dataset_size: 43799908

Dataset Card for [Dataset Name]

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage:
• Repository:https://gitlabdev.bridgeconn.com/software/research/datasets/snow-mountain
• Paper:https://arxiv.org/abs/2206.01205
• Leaderboard:
• Point of Contact:

Dataset Summary

The Snow Mountain dataset contains the audio recordings (in .mp3 format) and the corresponding text of The Bible in 11 Indian languages. The recordings were done in a studio setting by native speakers. Each language has a single speaker in the dataset. Most of these languages are geographically concentrated in the Northern part of India around the state of Himachal Pradesh. Being related to Hindi they all use the Devanagari script for transcription.

We have used this dataset for experiments in ASR tasks. But these could be used for other applications in speech domain, like speaker recognition, language identification or even as unlabelled corpus for pre-training.

Supported Tasks and Leaderboards

Atomatic speech recognition, Speaker recognition, Language identification

Languages

Hindi, Haryanvi, Bilaspuri, Dogri, Bhadrawahi, Gaddi, Kangri, Kulvi, Mandeali, Kulvi Outer Seraji, Pahari Mahasui

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

The Bible recordings were done in a studio setting by native speakers.

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@misc{https://doi.org/10.48550/arxiv.2206.01205, doi = {10.48550/ARXIV.2206.01205},

url = {https://arxiv.org/abs/2206.01205},

author = {Raju, Kavitha and V, Anjaly and Lish, Ryan and Mathew, Joel},

keywords = {Audio and Speech Processing (eess.AS), Machine Learning (cs.LG), Sound (cs.SD), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences},

title = {Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languages},

publisher = {arXiv},

year = {2022},

copyright = {Creative Commons Attribution Share Alike 4.0 International} }

Contributions

Thanks to @github-username for adding this dataset.