README.md

metadata

license: cc-by-4.0
language:
  - hi
  - gu
  - mr
  - ta
  - kn
  - te
  - ml
  - pa
  - or
  - bn
  - as
  - ur
  - en
pretty_name: Mann-ki-Baat
extra_gated_fields:
  Name: text
  Email: text
  Affiliation: text
  Position: text
size_categories:
  - 10K<n<100K
multilinguality:
  - multilingual
dataset_info:
  - config_name: en2indic
    features:
      - name: chunked_audio_filepath
        dtype: audio
      - name: text
        dtype: string
      - name: pred_text
        dtype: string
      - name: audio_filepath
        dtype: string
      - name: start_time
        dtype: float64
      - name: duration
        dtype: float64
      - name: alignment_score
        dtype: float64
      - name: hi_text
        dtype: string
      - name: hi_mining_score
        dtype: float64
      - name: gu_text
        dtype: string
      - name: gu_mining_score
        dtype: float64
      - name: mr_text
        dtype: string
      - name: mr_mining_score
        dtype: float64
      - name: ta_text
        dtype: string
      - name: ta_mining_score
        dtype: float64
      - name: kn_text
        dtype: string
      - name: kn_mining_score
        dtype: float64
      - name: te_text
        dtype: string
      - name: te_mining_score
        dtype: float64
      - name: ml_text
        dtype: string
      - name: ml_mining_score
        dtype: float64
      - name: pa_text
        dtype: string
      - name: pa_mining_score
        dtype: float64
      - name: or_text
        dtype: string
      - name: or_mining_score
        dtype: float64
      - name: bn_text
        dtype: string
      - name: bn_mining_score
        dtype: float64
      - name: as_text
        dtype: string
      - name: as_mining_score
        dtype: float64
      - name: ur_text
        dtype: string
      - name: ur_mining_score
        dtype: float64
      - name: mni_text
        dtype: string
      - name: mni_mining_score
        dtype: float64
    splits:
      - name: en2indic
        num_bytes: 506579823.11
        num_examples: 22494
    download_size: 473311809
    dataset_size: 506579823.11
  - config_name: indic2en
    features:
      - name: chunked_audio_filepath
        dtype: audio
      - name: text
        dtype: string
      - name: pred_text
        dtype: string
      - name: audio_filepath
        dtype: string
      - name: start_time
        dtype: float64
      - name: duration
        dtype: float64
      - name: alignment_score
        dtype: float64
      - name: en_text
        dtype: string
      - name: en_mining_score
        dtype: float64
    splits:
      - name: assamese
        num_bytes: 20785695
        num_examples: 13941
      - name: bengali
        num_bytes: 18327660
        num_examples: 20697
      - name: gujarati
        num_bytes: 18697394
        num_examples: 21280
      - name: hindi
        num_bytes: 18500205
        num_examples: 21280
      - name: kannada
        num_bytes: 18906793
        num_examples: 21095
      - name: malayalam
        num_bytes: 22105974
        num_examples: 24143
      - name: marathi
        num_bytes: 40613578
        num_examples: 45402
      - name: odia
        num_bytes: 35491866
        num_examples: 39928
      - name: punjabi
        num_bytes: 20683444
        num_examples: 8232
      - name: tamil
        num_bytes: 42440952
        num_examples: 39736
      - name: telugu
        num_bytes: 41511576
        num_examples: 52618
      - name: urdu
        num_bytes: 10459996
        num_examples: 14004
    download_size: 509056333
    dataset_size: 300244038
configs:
  - config_name: en2indic
    data_files:
      - split: en2indic
        path: En-Indic/train-*
  - config_name: indic2en
    data_files:
      - split: assamese
        path: Indic-En/asm/train-*
      - split: bengali
        path: Indic-En/ben/train-*
      - split: gujarati
        path: Indic-En/guj/train-*
      - split: hindi
        path: Indic-En/hin/train-*
      - split: kannada
        path: Indic-En/kan/train-*
      - split: malayalam
        path: Indic-En/mal/train-*
      - split: marathi
        path: Indic-En/mar/train-*
      - split: odia
        path: Indic-En/ory/train-*
      - split: punjabi
        path: Indic-En/pun/train-*
      - split: tamil
        path: Indic-En/tam/train-*
      - split: telugu
        path: Indic-En/tel/train-*
      - split: urdu
        path: Indic-En/urd/train-*

BhasaAnuvaad: A Speech Translation Dataset for 13 Indian Languages

Dataset Description

• Homepage: Bhasaanuvaad Collection
• Repository: Github
• Paper: BhasaAnuvaad: A Speech Translation Dataset for 13 Indian Languages

Overview

BhasaAnuvaad, is the largest Indic-language AST dataset spanning over 44,400 hours of speech and 17M text segments for 13 of 22 scheduled Indian languages and English.

This repository consists of parallel data for Speech Translation from Mann ki Baat, a subset of BhasaAnuvaad.

How to use

The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function.

• Before downloading first follow the following steps:

  1. Gain access to the dataset and get the HF access token from: https://huggingface.co/settings/tokens.
  2. Install dependencies and login HF:
     • Install Python
     • Run pip install librosa soundfile datasets huggingface_hub[cli]
     • Login by huggingface-cli login and paste the HF access token. Check here for details.

For example, to download the (indic2en or en2indic) config, simply specify the corresponding config name (i.e., "indic2en" for Hindi):

from datasets import load_dataset
bhasaanuvaad = load_dataset("ai4bharat/Mann-ki-Baat", "indic2en", split="hindi")

or

from datasets import load_dataset
bhasaanuvaad = load_dataset("ai4bharat/Mann-ki-Baat", "en2indic", split="en2indic")

Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.

from datasets import load_dataset
bhasaanuvaad = load_dataset("ai4bharat/Mann-ki-Baat", "indic2en", split="hindi", streaming=True)
print(next(iter(bhasaanuvaad)))

Citation

If you use BhasaAnuvaad in your work, please cite us:

@article{jain2024bhasaanuvaad,
  title   = {BhasaAnuvaad: A Speech Translation Dataset for 14 Indian Languages},
  author  = {Sparsh Jain and Ashwin Sankar and Devilal Choudhary and Dhairya Suman and Nikhil Narasimhan and Mohammed Safi Ur Rahman Khan and Anoop Kunchukuttan and Mitesh M Khapra and Raj Dabre},
  year    = {2024},
  journal = {arXiv preprint arXiv: 2411.04699}
}

License

This dataset is released under the CC BY 4.0.

Contact

For any questions or feedback, please contact:

• Raj Dabre (raj.dabre@cse.iitm.ac.in)
• Sparsh Jain (sjshiva8287@gmail.com)
• Ashwin Sankar (ashwins1211@gmail.com)
• Nikhil Narasimhan (nikhil.narasimhan99@gmail.com)
• Mohammed Safi Ur Rahman Khan (safikhan2000@gmail.com)

Please contact us for any copyright concerns.

Links

• GitHub Repository 💻
• Paper 📄
• Hugging Face Dataset 🤗