Datasets:
ArXiv:
License:
| pretty_name: 'Snow Mountain' | |
| language: | |
| - hi | |
| - bgc | |
| - kfs | |
| - dgo | |
| - bhd | |
| - gbk | |
| - xnr | |
| - kfx | |
| - mjl | |
| - kfo | |
| - bfz | |
| annotations_creators: | |
| - ? | |
| language_creators: | |
| - ? | |
| license: [] | |
| multilinguality: | |
| - multilingual | |
| size_categories: | |
| - | |
| 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](#table-of-contents) | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#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 | |
| |- cleaned | |
| |- lang1 | |
| |- book1_verse_audios.tar.gz | |
| |- book2_verse_audios.tar.gz | |
| ... | |
| ... | |
| |- all_verses.csv | |
| |- short_verses.csv | |
| |- 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. | |
| ## 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](https://github.com/<github-username>) for adding this dataset. | |