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---
license: cc-by-nc-4.0
task_categories:
- automatic-speech-recognition
language:
- hi
- mr
---
# ReadNet
## Dataset Description
ReadNet is an audio dataset collected from more than 2,00,000 children in the age group of 5-16 years in Hindi and Marathi language.
The dataset consists of audio files in the wav format where children read out [ASER Samples](https://asercentre.org/wp-content/uploads/2022/12/Hindi_ASER-2018.pdf) which consists of letters, words, stories and paragraphs in their native language.
This dataset is a subset of the larger dataset which consists of an estimated 2500 hours of data. This dataset consists of ~87 hours of audio data in Hindi and Marathi,
### Languages
Hindi and Marathi
```python
from datasets import load_dataset
dataset = load_dataset("PrathamOrgAI/ReadNet")
```
### Data Fields
- URL: URL of the audio file which can be downloaded in the .wav format
- Transcribed Text: Transcription of audio files in their respective language
### Annotations
Annotations for the audio files are provided in the Transcribed Text Column. The annotation of audio files was done on an annotation portal which was developed inhouse by Pratham Education Foundation
#### Annotation process
A team of annotators were hired for the annotation of audio
samples in two languages. Training regarding the annotation
portal was provided to the annotators. A dry run for the an-
notation was done initially and the annotation was reviewed
and feedback was given to the annotators to remove incon-
sistencies and errors in annotation. Annotation guidelines are
provided in the annotation portal itself, in both languages for
the annotators reference. Since the scale of the data set is
pretty large, we have decided to annotate some portion of the
data twice and the remaining data once depending on the re-
sources
#### Who are the annotators?
The Annotation was done by experts from the ASER team
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
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