Description

In the realm of spoken language processing, Speech-to-Text Translation (ST) holds a crucial role at the intersection of natural language processing. The primary aim of ST is to convert spoken language from one linguistic context into written text in another language. This typically involves using Automatic Speech Recognition (ASR) to convert speech in the source language into text, followed by Machine Translation (MT) to translate the source language text into the target language. ST is a multimodal task that takes speech input and produces output in text format. Furthermore, it is inherently multilingual, taking speech input in one language and generating text output in another.Traditionally, human language translators proficient in both the source and target languages have handled this task. However, the scarcity of translators fluent in multiple languages has created a pressing need for a dedicated model tailored to excel in the unique realm of ST tasks across diverse languages. Recent advancements in ST have predominantly focused on high-resource languages, leaving a significant gap for low-resource languages that face a substantial catch-up journey. The attention imbalance is primarily due to the scarcity of data for low-resource languages, as most deep-learning models depend on data abundance. Acquiring such data for low-resource languages poses a formidable challenge.

While a considerable body of research is dedicated to ST across diverse language families, there is a noticeable gap in investigating this domain concerning low-resource Indian languages. Currently, there are no datasets specifically designed for the ST task in Indian languages, covering both the Indo-Aryan and the Dravidian language families. The goal of this research is to create either an End-to-End (E2E) or a Cascaded Speech-to-Text (ST) model that encompasses all Indian languages.

The aim of this Indic-track shared task is to establish a speech translation model that spans a diverse array of dialects and low-resource languages originating from the Indo-Aryan and Dravidian language families in India. Given that a significant portion of the data is sourced from very low-resource languages, these languages remain largely unexplored in the realm of speech translation. Compounding this challenge is the fact that many of the target languages are distantly related to English. Consequently, we anticipate that relying solely on pre-trained models may encounter numerous obstacles. The dataset provided will serve as the inaugural benchmark and gold standard dataset, encompassing all major Indian languages. Our aspiration is for participants to develop systems capable of real-world deployment in the future.

Data & Baselines

The ST task data for the Indic-track will encompass three Indian languages representing diverse language families. The languages included in this shared task are Hindi (hi), Bengali (bn), and Tamil (ta), originating from the Indo-Aryan and Dravidian language families. The dataset will include speeches and texts (transcriptions) in English (source language) and texts (translations) in Hindi, Bengali, and Tamil (target languages).

The data for this Indic-track shared task comprises a Speech-to-Text (ST) corpus that includes 3 low-resource Indian languages. Consistency is maintained across all corpora, with an equal number of lines in their .en, .lang, and .yaml files. However, due to inherent linguistic differences, the number of tokens in the .en and .lang files varies. The count of audio files corresponds to the number of distinct talks, each delivered by an individual speaker. Additionally, the speech hours indicate the cumulative duration of speech in a given language. Each of these parameters is meticulously categorized into test, train, and valid subsets, establishing a comprehensive and structured dataset.

English to Hindi and Hindi to English (en <-> hi)

Hindi is the third most spoken language in the world, with 615 million speakers. It belongs to Indo-Aryan language family, mainly spoken in India. It is also the official language of India, written in Devnagiri script. The data contains English speech, English texts (transcripts), and Hindi texts (translations).

English to Bengali and Bengali to English (en <-> bn)

Bengali is the 7th most spoken language in the world, with 228 million speakers. It belongs to Indo-Aryan language family, spoken in Bengal region of South-Asia. It is also the official language of Bangladesh, written in Bengali-Assamese script. The data contains English speech, English texts (transcripts), and Bengali texts (translations).

English to Tamil and Tamil to English (en <-> ta)

Tamil is one of the classical languages of India, spoken by 90.8 million speakers. It belongs to Dravidian language family, spoken by Tamil people of South-Asia. It is the official language of Tamil Nadu state of India, written in Brahmi script. The data contains English speech, English texts (transcripts), and Tamil texts (translations).

To Download the Datasets, please fill this form: Google Form

Test Data :-

Test data will be released soon.

Submission

The submissions must be mailed to this email: iwsltindictracksubmissions@gmail.com

Only one submission is allowed per team. The submissions must be submitted zipped in tar.gz format and then emailed. The email should include the following information:

  • Institute:

  • Contact Person (mail ID):

  • Team Members with Details:

  • Brief abstract about the system (Mention details about all the models if a different model is used for different language pair):

Language specific (must mention for each language pair)

  • Data condition: Constrained/Unconstrained

  • End-to-End or Cascaded ST Model:

  • Multilingual: Yes/No

  • Do you want to make your submissions freely available for research purposes? (yes/no)

TAR archive file structure:

For language specific submission:
< TeamID >_< STModel >_<DataCon>_< IndicYear >.tar.gz  
  /< LangPair >_< STModel >_< IndicYear >.<Tgt>
  /...

For multilingual submission:
< TeamID >_< STModel >_<DataCon>_< IndicYear >.tar.gz  
  /< LangPair >_< STModel >_< IndicYear >.hi
  /< LangPair >_< STModel >_< IndicYear >.bn
  /< LangPair >_< STModel >_< IndicYear >.ta

where:

<TeamID> is the Team ID used at the time of filling google form for downloading datasets.

<LangPair> denotes language pair, example: **‘en-hi’, ‘en-bn’, ‘en-ta’, ‘hi-en’, ‘bn-en’, ‘ta-en’ **.

<STModel> denotes whether the ST model is cascaded or end-to-end, example: ‘Casc’ or ‘E2E’.

<DataCon> denotes whether the data used for model training is constrained or unconstrained, example: ‘Cons’ or ‘UnCons’.

<IndicYear> is the year of submission for Indic track, example: ‘Indic2024’.

<Tgt> denotes the file extension of target language, example: ‘hi’ for hindi, ‘bn’ for bengali and ‘ta’ for tamil.

For Example: IITI_en-hi_E2E_Indic2024.hi

Submission Criterion

The submissions for the all the language-pairs can for the below 2 conditions:

  1. Constrained Conditions: Only the language data provided with the data here can be used for pre-training the models. No data from any other sources can be used for pre-training of the models.

  2. Unconstrained Conditions: Any pre-trained models and any other data can be used for pre-training the models.

Evaluation

  • sacre-BLEU is used for the evaluations.

  • Only one test set will be provided for each language pair, consisting speech in the source language only. The test set will contain only the .wav files and a .yaml file containing the segmentation of these wav files.

  • For every sentence of the speech, the result txt file must contain only the predicted translation per line in the target language. All submissions must contain this result txt file for evaluation.

Timeline

We follow the same schedule as all the other tasks. (Important Dates)

Organizers

  1. Nivedita Sethiya (PhD Scholar, AI Lab, Computer Science and Engineering, Indian Institute of Technology Indore, India)- phd2201201003@iiti.ac.in
  2. Ashwin Sankar (MS Student, Indian Institute of Technology Madras; Researcher, AI4Bharat) - da24s022@smail.iitm.ac.in
  3. Raj Dabre ( Adjunct Professor, Indian Institute of Technology Madras; Researcher AI4Bharat) - raj.dabre@cse.iitm.ac.in
  4. Dr. Chandresh Kumar Maurya (Assistant Professor, AI Lab, Computer Science and Engineering, Indian Institute of Technology Indore, India)- chandresh@iiti.ac.in

Contact

Chair: Dr. Chandresh Kumar Maurya

Discussion: iwslt-evaluation-campaign@googlegroups.com