Datasets:

project-boli
/

pangal

configs:
  - config_name: default
    data_files:
      - split: preview
        path: data/preview/*
license: cc-by-nc-sa-4.0
task_categories:
  - automatic-speech-recognition
language:
  - mni
tags:
  - mni
  - automatic-speech-recognition
  - asr
  - life app
  - audio
  - speech
pretty_name: Boli Pangal Data Transcription
description: "### The Dataset\r\n\r\nThe current data preview of Pangal is being released as part of the **[Project BoLI](https://boli.unreal-tece.co.in)**. This preview is a reflection of the full dataset and consists of the following -\r\n1. Speech Recordings of 200 sentences in the language.\r\n2. Transcriptions in IPA and Bangali, English\r\n3. Translations in English (which also act as prompts for the translation sentences)\r\n4. Detailed speaker metadata, including their demographic, educational and linguistic profile.\r\n5. Prompt in English and Hindi.\r\n\r\nThe full dataset contains the following -\r\n1. Translations of a minimum of 1000 carefully selected sentences. These sentences are selected to represent diverse morphosyntactic categories generally found in Indian languages such as demonstratives, classifiers, TAM morphology, and different sentence structures such as transitives and ditransitives. These sets of sentences are based on the standardised questionnaires built by Linguists for writing the sketch grammar of any human language, thereby, representing almost full range of morphosyntactic properties exhibited in the language. Unlike other benchmarks which focus largely on lexical level evaluation (aka domains), this is the first benchmark dataset that evaluates model's performance on a range of morphosyntactic structures, even the most uncommon ones.\r\n2. More than 600 narrative speeches collected across 8 domains. These are recorded by at least 2 speakers. Narrations, along with their prompts, can be used to evaluate AI models on a range of prompt-based tasks.\r\n3. Along with transcriptions in IPA and other scripts and translation, all data is interlinearly glossed at morphemic level. This gives a word-by-word meaning and morphosyntactic information, thereby, enabling evaluation of models on their deep grammatical knowledge, ability for cross-linguistic comparison and generalisation and reasoning capacity and skills in language-related puzzles. This allows for evaluating the model's capacity on reasoning tasks beyond mathematical reasoning tasks as well as their capacity to arrive at typological generalisations.\r\n4. In addition to the data and prompt itself, as mentioned earlier, we are also making available the detailed speaker metadata and prompt-level metadata viz domain, elicitation method, multilingual prompt, target grammatical category (for translation sentences), etc.\r\n5. In accordance with our data governance policy, all contributors to the dataset, including those recording the dataset and those transcribing it, are named and listed as data contributors.\r\n\r\n### Dataset Preparation\r\nThe speech included as part of this dataset was recorded by native speakers using a data collection application on a mobile device (Karya or in-house app, Atekho), by translating sentences from English or Hindi to the target language.\r\n\r\nThe recorded speech was validated and then transcribed manually by trained linguists, working with the native speakers, following the [guidelines for the project](https://docs.google.com/document/d/1h-EcxxaNiGzdWiwc1-V_S-Xt6n4MwE6F/edit?usp=sharing&ouid=106672251267017181111&rtpof=true&sd=true) using MATra Lab, a part of the [LiFE Suite Ecosystem](https://life.unreal-tece.co.in), developed by [Unreal Tece LLP](https://unreal-tece.co.in).\r\n\r\n[Project BoLI Guidelines](https://docs.google.com/document/d/1h-EcxxaNiGzdWiwc1-V_S-Xt6n4MwE6F/edit?usp=sharing&ouid=106672251267017181111&rtpof=true&sd=true)\r\n\r\n\r\n### About Pangal\r\n\r\nPangal is a Tibeto-Burman language, spoken primarily in Imphal East, Manipur by 3,58,000 speakers as per Census 2011 / other sources. The current dataset is primarily recorded by speakers from New Checkon, Imphal East, Manipur. Phonetically, Pangal features a basic six-vowel system: /i/, /e/, /a/, /u/, /o/, and /ə/. The vowel phonemes are categorised by four levels of height (high, mid-high, mid, and low) and three levels of backness (front, central, and back). The language also has six distinct diphthongs - /əi/, /əu/, /ai/, /oi/, /au/, /ui/. Pangal inherits the core 15 native consonants of Proto-Meitei, expanding its phonetic inventory to include voiced segments due to historical Indo-Aryan and bilingual contact. The consonant speech sounds recorded in this dataset are- /p/, /pʰ/, /b/,/t/, /tʰ/, /d/, /k/, /kʰ/, /g/, /m/, /n/, /ŋ/, /r/, /s/, /h/, /l/, /j/, /t͡ʃ/, /d͡ʒ/, /w/. Voiced aspirated consonants are absent in native elements. The syllable structure is either CV (Consonant-Vowel) or CVC (Consonant-Vowel-Consonant), with no complex coda. Pangal utilises a binary tonal contrast (Level versus Falling tone) to distinguish lexical meanings. A unique phonological rule involves an extended voicing process: in standard Meitei, voiceless stops undergo voicing after voiced segments (e.g., the suffix -pa becomes -ba), whereas in the Pangal variety, this rule applies even when preceded by an unaspirated voiceless stop (e.g., standard kap-pa shifts toward a more heavily voiced, localised pronunciation such as kabba).\r\n\r\nMorphologically, Pangal is highly agglutinative and predominantly suffixing. Words are formed by attaching grammatical suffixes to a stable root. For example, the noun ‘ima’ (mother) can take the plural suffix ‘-sing’ to become ‘ima-sing’ (mothers). Similarly, the verb root ‘tʃa’ (eat) can be combined with the past tense marker ‘-re’ to form ‘tʃa-re’ (ate), and with the incomplete negation suffix ‘-dari’ to form ‘tʃa-dari’ (has not eaten yet). There is no agreement marking; grammatical gender, number, and person do not trigger structural modifications on the verb. Gender is indicated lexically, using markers such as -nupa (male) and -nupi (female), as in ‘matʃa-nupa’ (son) and ‘matʃa-nupi’ (daughter). The language exhibits a simplified lexical category system, with only two major open word classes: nouns and verbs. Adjectives and adverbs are not independent categories but are derived morphologically from verbal roots. For instance, the adjective meaning ‘long’ can be formed from the verb root ‘saŋ’ (to be long) by adding prefix ə- and the participial suffix -ba resulting in ə-saŋ-ba (long).\r\n\r\nThe Pangal variety follows a Subject-Object-Verb (SOV) word order. For example: /əina (I- NOM) tʃak (rice) tʃari (eating)/ which means ‘I am eating food’. Despite this structural baseline, the language exhibits pragmatic flexibility. Important thematic elements may be fronted to the beginning of a sentence for emphasis. For instance, to highlight the object through fronting, a speaker might utilise an OSV structure such as /tʃak əina tʃari/ (Food, I  am eating), thereby focusing primary attention on the object.\r\n\r\n\r\n### About Project BoLI\r\n\r\n[Project BoLI](https://boli.unreal-tece.co.in) is the flagship project of [UnReaL-TecE LLP](https://unreal-tece.co.in), which aims to build high-quality datasets for benchmarking and evaluating different kinds of AI tasks, including speech-to-text, machine translation, grammatical analysis and reasoning tasks and prompt-based evaluation of LLMs. While the project aims to build these datasets for every Indian language and variety, the primary focus is on over 1300 underserved languages and both first and second language varieties of major, scheduled languages. The project's uniqueness is not just limited to the kind of benchmarking tasks it supports (including proposing some novel tasks) and also the kind of languages and communities it supports but also in its contextualisation and implementation of a unique data governance model (not yet implemented anywhere across the globe), which mandates that all datasets released by the project and their derivatives (including the models) are co-owned by the community members and all contributors of the project, and any permission to use the dataset is not a transfer of ownership but a revocable license to use it. The conditions under which the license could be revoked are clearly mentioned as part of the BoLI License. The complete details of the project, languages and communities supported till now, the quantum of data available till now, its data governance model and other relevant documents are all publicly accessible at the [project website](https://boli.unreal-tece.co.in).\r\n\r\n### Ethical Considerations, Consent, IPR and Attribution\r\n\r\nProject BoLI and this repository represents our commitment to not only fair remuneration to the speakers of the language but also to co-ownership and equal IPR to all the contributors who have built the dataset. We believe this is the first step to move away from the extractive data collection and use practices and ensure fairness in our treatment of the community members. As such, we have listed all speakers and transcribers as Contributors to the dataset (we insist that they are co-owners of the dataset, even though the HuggingFace platform does not provide us an explicit way of stating that) and they are further recognised as Speakers and Annotators of the dataset. This dataset is only licensed to other researchers for use in their research projects. More details about licensing and commercial use conditions are given in the License and Commercial Use sections.\r\n\r\n[Project BoLI - Data Governance Policy](https://docs.google.com/document/d/1DBGZVFJ4RG57j4rFphKIrvgbpfpj8HqZ/edit?usp=sharing&ouid=106672251267017181111&rtpof=true&sd=true)\r\n\r\n[BoLI Ethics Principle & Pledge](https://docs.google.com/document/d/1hMQzwsOJF4QDfk_DOSIGLOF3GApLQOZA/edit?usp=sharing&ouid=106672251267017181111&rtpof=true&sd=true)\r\n\r\n[Project BoLI - Digital Consent Form](https://docs.google.com/document/d/1BTQUrgVTS-sVszUUDOkbnyUNusAHVYm4/edit?usp=sharing&ouid=106672251267017181111&rtpof=true&sd=true)\r\n\r\n[Project BoLI - Field Recording of Oral Consent](https://docs.google.com/document/d/1xoRxXcNES2oQqBajY3Z7HxMwpt0oOyDK/edit?usp=sharing&ouid=106672251267017181111&rtpof=true&sd=true)\r\n\r\n[Project BoLI - TnC](https://docs.google.com/document/d/1UuiIAHWXXKF202MYqLrtOy1ftUk__POW/edit?usp=sharing&ouid=106672251267017181111&rtpof=true&sd=true)\r\n\r\n\r\n### Dataset Access\r\n\r\nFull dataset for the language can be browsed and queried on our [app](https://life.unreal-tece.co.in). The results of the evaluation of different models will also be made available on the same link. If you would like to access the full dataset for your own use or would like to work with us in collecting more data for any language or variety, or collaborate with us in this initiative in any other way, please get in touch with us."
homepage: https://lifeapp.unreal-tece.co.in/projects/D_BoLI_Pangal_Data_Transcription
citation: ''

Boli Pangal Data Transcription

Dataset Description

Project: Boli Pangal Data Transcription
Contributors: Hasina Syed Hasinasyed, BoLI

About

The Dataset

The current data preview of Pangal is being released as part of the Project BoLI. This preview is a reflection of the full dataset and consists of the following -

Speech Recordings of 200 sentences in the language.
Transcriptions in IPA and Bangali, English
Translations in English (which also act as prompts for the translation sentences)
Detailed speaker metadata, including their demographic, educational and linguistic profile.
Prompt in English and Hindi.

The full dataset contains the following -

Translations of a minimum of 1000 carefully selected sentences. These sentences are selected to represent diverse morphosyntactic categories generally found in Indian languages such as demonstratives, classifiers, TAM morphology, and different sentence structures such as transitives and ditransitives. These sets of sentences are based on the standardised questionnaires built by Linguists for writing the sketch grammar of any human language, thereby, representing almost full range of morphosyntactic properties exhibited in the language. Unlike other benchmarks which focus largely on lexical level evaluation (aka domains), this is the first benchmark dataset that evaluates model's performance on a range of morphosyntactic structures, even the most uncommon ones.
More than 600 narrative speeches collected across 8 domains. These are recorded by at least 2 speakers. Narrations, along with their prompts, can be used to evaluate AI models on a range of prompt-based tasks.
Along with transcriptions in IPA and other scripts and translation, all data is interlinearly glossed at morphemic level. This gives a word-by-word meaning and morphosyntactic information, thereby, enabling evaluation of models on their deep grammatical knowledge, ability for cross-linguistic comparison and generalisation and reasoning capacity and skills in language-related puzzles. This allows for evaluating the model's capacity on reasoning tasks beyond mathematical reasoning tasks as well as their capacity to arrive at typological generalisations.
In addition to the data and prompt itself, as mentioned earlier, we are also making available the detailed speaker metadata and prompt-level metadata viz domain, elicitation method, multilingual prompt, target grammatical category (for translation sentences), etc.
In accordance with our data governance policy, all contributors to the dataset, including those recording the dataset and those transcribing it, are named and listed as data contributors.

Dataset Preparation

The speech included as part of this dataset was recorded by native speakers using a data collection application on a mobile device (Karya or in-house app, Atekho), by translating sentences from English or Hindi to the target language.

The recorded speech was validated and then transcribed manually by trained linguists, working with the native speakers, following the guidelines for the project using MATra Lab, a part of the LiFE Suite Ecosystem, developed by Unreal Tece LLP.

Project BoLI Guidelines

About Pangal

Pangal is a Tibeto-Burman language, spoken primarily in Imphal East, Manipur by 3,58,000 speakers as per Census 2011 / other sources. The current dataset is primarily recorded by speakers from New Checkon, Imphal East, Manipur. Phonetically, Pangal features a basic six-vowel system: /i/, /e/, /a/, /u/, /o/, and /ə/. The vowel phonemes are categorised by four levels of height (high, mid-high, mid, and low) and three levels of backness (front, central, and back). The language also has six distinct diphthongs - /əi/, /əu/, /ai/, /oi/, /au/, /ui/. Pangal inherits the core 15 native consonants of Proto-Meitei, expanding its phonetic inventory to include voiced segments due to historical Indo-Aryan and bilingual contact. The consonant speech sounds recorded in this dataset are- /p/, /pʰ/, /b/,/t/, /tʰ/, /d/, /k/, /kʰ/, /g/, /m/, /n/, /ŋ/, /r/, /s/, /h/, /l/, /j/, /t͡ʃ/, /d͡ʒ/, /w/. Voiced aspirated consonants are absent in native elements. The syllable structure is either CV (Consonant-Vowel) or CVC (Consonant-Vowel-Consonant), with no complex coda. Pangal utilises a binary tonal contrast (Level versus Falling tone) to distinguish lexical meanings. A unique phonological rule involves an extended voicing process: in standard Meitei, voiceless stops undergo voicing after voiced segments (e.g., the suffix -pa becomes -ba), whereas in the Pangal variety, this rule applies even when preceded by an unaspirated voiceless stop (e.g., standard kap-pa shifts toward a more heavily voiced, localised pronunciation such as kabba).

Morphologically, Pangal is highly agglutinative and predominantly suffixing. Words are formed by attaching grammatical suffixes to a stable root. For example, the noun ‘ima’ (mother) can take the plural suffix ‘-sing’ to become ‘ima-sing’ (mothers). Similarly, the verb root ‘tʃa’ (eat) can be combined with the past tense marker ‘-re’ to form ‘tʃa-re’ (ate), and with the incomplete negation suffix ‘-dari’ to form ‘tʃa-dari’ (has not eaten yet). There is no agreement marking; grammatical gender, number, and person do not trigger structural modifications on the verb. Gender is indicated lexically, using markers such as -nupa (male) and -nupi (female), as in ‘matʃa-nupa’ (son) and ‘matʃa-nupi’ (daughter). The language exhibits a simplified lexical category system, with only two major open word classes: nouns and verbs. Adjectives and adverbs are not independent categories but are derived morphologically from verbal roots. For instance, the adjective meaning ‘long’ can be formed from the verb root ‘saŋ’ (to be long) by adding prefix ə- and the participial suffix -ba resulting in ə-saŋ-ba (long).

The Pangal variety follows a Subject-Object-Verb (SOV) word order. For example: /əina (I- NOM) tʃak (rice) tʃari (eating)/ which means ‘I am eating food’. Despite this structural baseline, the language exhibits pragmatic flexibility. Important thematic elements may be fronted to the beginning of a sentence for emphasis. For instance, to highlight the object through fronting, a speaker might utilise an OSV structure such as /tʃak əina tʃari/ (Food, I am eating), thereby focusing primary attention on the object.

About Project BoLI

Project BoLI is the flagship project of UnReaL-TecE LLP, which aims to build high-quality datasets for benchmarking and evaluating different kinds of AI tasks, including speech-to-text, machine translation, grammatical analysis and reasoning tasks and prompt-based evaluation of LLMs. While the project aims to build these datasets for every Indian language and variety, the primary focus is on over 1300 underserved languages and both first and second language varieties of major, scheduled languages. The project's uniqueness is not just limited to the kind of benchmarking tasks it supports (including proposing some novel tasks) and also the kind of languages and communities it supports but also in its contextualisation and implementation of a unique data governance model (not yet implemented anywhere across the globe), which mandates that all datasets released by the project and their derivatives (including the models) are co-owned by the community members and all contributors of the project, and any permission to use the dataset is not a transfer of ownership but a revocable license to use it. The conditions under which the license could be revoked are clearly mentioned as part of the BoLI License. The complete details of the project, languages and communities supported till now, the quantum of data available till now, its data governance model and other relevant documents are all publicly accessible at the project website.

Ethical Considerations, Consent, IPR and Attribution

Project BoLI and this repository represents our commitment to not only fair remuneration to the speakers of the language but also to co-ownership and equal IPR to all the contributors who have built the dataset. We believe this is the first step to move away from the extractive data collection and use practices and ensure fairness in our treatment of the community members. As such, we have listed all speakers and transcribers as Contributors to the dataset (we insist that they are co-owners of the dataset, even though the HuggingFace platform does not provide us an explicit way of stating that) and they are further recognised as Speakers and Annotators of the dataset. This dataset is only licensed to other researchers for use in their research projects. More details about licensing and commercial use conditions are given in the License and Commercial Use sections.

Project BoLI - Data Governance Policy

BoLI Ethics Principle & Pledge

Project BoLI - Digital Consent Form

Project BoLI - Field Recording of Oral Consent

Project BoLI - TnC

Dataset Access

Full dataset for the language can be browsed and queried on our app. The results of the evaluation of different models will also be made available on the same link. If you would like to access the full dataset for your own use or would like to work with us in collecting more data for any language or variety, or collaborate with us in this initiative in any other way, please get in touch with us.

Tools

We employed Karya and Atekho for collecting and recording data. The complete dataset is transcribed and exported using MATra Lab. Both Atekho and MAtra Lab are part of the LiFE Suite Ecosystem, developed by Unreal Tece LLP.

Speakers

Hasina Syed

Annotators

BoLI, Hasinasyed

Structure

The dataset is organized by splits (e.g. train, test, validation).

Each row contains audio, audio-level metadata, prompt metadata and speaker metadata as described below:

Audio and Audio-level Metadata

audio: The audio file path (loaded as Audio feature in HF Datasets)
audio_id: Unique identifier for the audio
filename: Original filename
sentence-<SCRIPT>-transcription: Text transcription of the audio in the given script
speaker_id: Identifier for the speaker
boundaryID: Identifier for the boundary
start_time: Start time of the segment in seconds
end_time: End time of the segment in seconds

Prompt Metadata

'Q_Id`: Unique identifier for the question or prompt associated with the audio (maps to the question in the LiFE Questionnaire projects and accessible through the questionnaire repo)
'Domain': Domain of the audio (e.g., Agriculture, Education, General, etc.)
'Elicitation_Method`: Method used to elicit the speech (e.g., Translation, Narration, etc.)
Target: An optional field for translation indicating the grammatical structure being targeted for elicitation using the sentence.

Speaker Metadata

ageGroup: Age group of the speaker (e.g., 18-30, 30-50, etc.)
gender: Gender of the speaker
educationLevel: Education level of the speaker
educationMediumUpto12-list: Medium of education up to 12th grade (list of comma-separated values)
'educationMediumAfter12-list`: Medium of education after 12th grade (list of comma-separated values)
otherLanguages-list: Languages spoken by the speaker (list of comma-separated values) - this usually excludes the primary language of the dataset and is used to capture multilingualism in speakers.
nativeLanguage: The native language of the speaker (optional field if data is collected from non-native speakers of the language)
placeOfRecording: The location where the audio was recorded (optional field) or the native place of the speaker (if known)
typeOfplace: Whether the placeOfRecording mentioned is City, Town or Village.

Additional Metadata

textgrid_json: TextGrid data converted to JSON format In addition to any other metadata fields provided during upload are optionally included.

License

This work is licensed under a CC-By-NC-SA-4.0 license. This license allows reusers to distribute, remix, adapt, build upon, and incorporate into software systems, the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. If you remix, adapt, build upon, or incorporate into software systems, you must license the modified material, including material generated by the software system, under identical terms, and license the software system under the GNU General Public License.

Commercial Use

If you are interested in using this dataset for commercial purposes, please contact us (contact [at] unreal-tece[dot]co[dot]in). Profits from commercial licensing will be distributed as royalties to the community members who contributed to this dataset.

Contact

For questions, issues, or contributions, open an issue on the dataset repository or contact us directly (contact [at] unreal-tece[dot]co[dot]in).