Datasets:
pretty_name: Snow Mountain
language:
- hi
- bgc
- kfs
- dgo
- bhd
- gbk
- xnr
- kfx
- mjl
- kfo
- bfz
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:
- hi
- bgc
dataset_info:
- config_name: hi
features:
- name: Unnamed
dtype: int64
- name: sentence
dtype: string
- name: path
dtype: string
splits:
- name: train_500
num_examples: 400
- name: val_500
num_examples: 100
- name: train_1000
num_examples: 800
- name: val_1000
num_examples: 200
- name: test_common
num_examples: 500
dataset_size: 71.41 hrs
- config_name: bgc
features:
- name: Unnamed
dtype: int64
- name: sentence
dtype: string
- name: path
dtype: string
splits:
- name: train_500
num_examples: 400
- name: val_500
num_examples: 100
- name: train_1000
num_examples: 800
- name: val_1000
num_examples: 200
- name: test_common
num_examples: 500
dataset_size: 27.41 hrs
Dataset Card for [snow-mountain]
Table of Contents
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
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, Malayalam,Kannada, Tamil, Telugu
Dataset Structure
data
|- cleaned
|- lang1
|- book1_verse_audios.tar.gz
|- book2_verse_audios.tar.gz
...
...
|- all_verses.tar.gz
|- short_verses.tar.gz
|- lang2
...
...
|- experiments
|- lang1
|- train_500.csv
|- val_500.csv
|- test_common.csv
...
...
|- lang2
...
...
|- raw
|- lang1
|- chapter1_audio.mp3
|- chapter2_audio.mp3
...
...
|- text
|- book1.csv
|- book1.usfm
...
...
|- lang2
...
...
Data Instances
A typical data point comprises the path to the audio file, usually called path and its transcription, called sentence.
{'sentence': 'क्यूँके तू अपणी बात्तां कै कारण बेकसूर अर अपणी बात्तां ए कै कारण कसूरवार ठहराया जावैगा',
'audio': {'path': 'data/cleaned/haryanvi/MAT/MAT_012_037.wav',
'array': array([0., 0., 0., ..., 0., 0., 0.]),
'sampling_rate': 16000},
'path': 'data/cleaned/haryanvi/MAT/MAT_012_037.wav'}
Data Fields
path: The path to the audio file
audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: dataset[0]["audio"] the audio file is automatically decoded and resampled to dataset.features["audio"].sampling_rate. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the "audio" column, i.e. dataset[0]["audio"] should always be preferred over dataset["audio"][0].
sentence: the transcription of the audio file.
Data Splits
We create splits of the cleaned data for training and analysing the performance of ASR models. The splits are available in the experiments directory. The file names indicate the experiment and the split category. Additionally two csv files are included in the data splits - all_verses and short_verses. Various data splits were generated from these main two csvs. short_verses.csv contains audios of length < 10s and corresponding transcriptions where all_verses.csv contains complete cleaned verses including long and short audios. Due to the large size (>10MB), keeping these csvs as tar in cleaned folder.
Dataset Loading
raw folder has chapter wise audios in mp3 format. For doing experiments, we might need audios in wav format. Verse wise audios are keeping in cleaned folder in wav format. So our dataset size is much higher and hence loading might take some time. Here is the approximate time needed for laoding the Dataset.
- Hindi (OT books) ~20 minutes
- Hindi minority languages (NT books) ~9 minutes
- Dravidian languages (OT+NT books) ~30 minutes
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
The data is licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)
Citation Information
@inproceedings{Raju2022SnowMD, title={Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languages}, author={Kavitha Raju and V. Anjaly and R. Allen Lish and Joel Mathew}, year={2022} }
Contributions
Thanks to @github-username for adding this dataset.