Update Readme (#2)

- Update Readme (4b3db556985279ce7885a8266f1062f0f7eb1d97)

README.md

@@ -1,127 +1,96 @@
 ---
-pretty_name: 'Snow Mountain'
+pretty_name: Snow Mountain
 language:
-  - hi
-  - bgc
-  - kfs
-  - dgo
-  - bhd
-  - gbk
-  - xnr
-  - kfx
-  - mjl
-  - kfo
-  - bfz
+- hi
+- bgc
+- kfs
+- dgo
+- bhd
+- gbk
+- xnr
+- kfx
+- mjl
+- kfo
+- bfz
 annotations_creators:
-- ?
+- 'null': null
 language_creators:
-- ?
-license: []
+- 'null': null
 multilinguality:
 - multilingual
-size_categories:
-- 
 source_datasets:
 - Snow Mountain
-tags: []
 task_categories:
 - automatic-speech-recognition
+- text-to-speech
 task_ids: []
 configs:
-  - hi
-  - bgc
+- 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
-
+- 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)
+# snow-mountain
 
 ## Dataset Description
 
 - **Homepage:**
-- **Repository:https://gitlabdev.bridgeconn.com/software/research/datasets/snow-mountain**
 - **Paper:https://arxiv.org/abs/2206.01205**
-- **Leaderboard:**
-- **Point of Contact:**
+- **Point of Contact:Joel Mathew**
 
 ### 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. 
+The Snow Mountain dataset contains the audio recordings (in .mp3 format) and the corresponding text of The Bible (contains both Old Testament (OT) and New Testament (NT)) 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
+Atomatic speech recognition, Speech-to-Text, Speaker recognition, Language identification
 
 ### Languages
 
-Hindi, Haryanvi, Bilaspuri, Dogri, Bhadrawahi, Gaddi, Kangri, Kulvi, Mandeali, Kulvi Outer Seraji, Pahari Mahasui, Malayalam,Kannada, Tamil, Telugu 
+Hindi, Haryanvi, Bilaspuri, Dogri, Bhadrawahi, Gaddi, Kangri, Kulvi, Mandeali, Kulvi Outer Seraji, Pahari Mahasui, Malayalam, Kannada, Tamil, Telugu 
 
 ## Dataset Structure
 ```
@@ -164,7 +133,7 @@ data
 ```
 ### Data Instances
 
-A typical data point comprises the path to the audio file, usually called path and its transcription, called sentence. 
+A data point comprises of the path to the audio file, called `path` and its transcription, called `sentence`. 
 
 ```
 {'sentence': 'क्यूँके तू अपणी बात्तां कै कारण बेकसूर अर अपणी बात्तां ए कै कारण कसूरवार ठहराया जावैगा',
@@ -176,73 +145,24 @@ A typical data point comprises the path to the audio file, usually called path a
 
 ### Data Fields
 
-path: The path to the audio file
+`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].
+`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.
+`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.
+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. `all_verses.csv` contains complete cleaned verses including long and short audios. Due to the large size (>10MB), we keep these CSVs compressed in the `tar.gz format in the `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
+`raw` folder has chapter wise audios in .mp3 format. For doing experiments, we might need audios in .wav format. Verse wise audio files are keept in the `cleaned` folder in .wav format. This results in a much larger size which contributes to longer loading time into memory. Here is the approximate time needed for loading the Dataset.
+  - Hindi (OT books): ~20 minutes
+  - Hindi minority languages (NT books): ~9 minutes
+  - Dravidian languages (OT+NT books): ~30 minutes
 
-[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]
+## Details
+Please refer to the paper for more details on the creation and the rationale for the splits we created in the dataset.
 
 ### Licensing Information
 
@@ -251,14 +171,12 @@ The data is licensed under the Creative Commons Attribution-ShareAlike 4.0 Inter
 
 ### Citation Information
 
+Please cite this work if you make use of it:
+
+```
 @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.
+```