Introduction

Welcome to the English-Tamil Bilingual Parallel Corpora dataset for the Banking, Financial Services, and Insurance(BFSI) domain. This meticulously curated dataset offers a rich collection of bilingual sentence pairs translated between English and Tamil. It serves as a valuable resource for developing domain-specific machine translation systems, language models, and NLP applications within the BFSI sector.

Dataset Content

Domain-Specific Content

Format and Structure

Usage and Applications

Introduction

This Tamil Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Tamil -speaking Real Estate customers. With over 30 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 30 hours of dual-channel call center recordings between native Tamil speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

Topic Diversity

This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

Such domain-rich variety ensures model generalization across common real estate support conversations.

Transcription

All recordings are accompanied by precise, manually verified transcriptions in JSON format.

These transcriptions streamline ASR and NLP development for Tamil real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

This enables smart filtering, dialect-focused model training, and structured dataset exploration.

Usage and Applications

This dataset is ideal for voice AI and NLP systems built for the real estate sector:

The dataset is a carefully selected set of Tamil film reviews with the goal of advancing NLP research in the areas of text classification, sentiment analysis, and aspect-based sentiment analysis. We have invited users to review twenty-five films using a Google form. Additional reviews were taken from websites such as IMDb and YouTube. From the list of selected aspects, we also made sure that the review collection was based on the presence of at least one target aspect, including cinematography, acting, screenplay, story, director, songs, background music, and editing. About 1,390 reviews total, tagged for positive as well as negative views across eight different categories, make up the dataset.

Dataset Card for Dataset Name

This dataset is designed for fine-tuning Large Language Models (LLMs) in Tamil, enabling them to understand and generate high-quality Tamil text across multiple domains. It contains 72,000 curated and generated samples, ensuring a rich linguistic diversity that improves model generalization. 🔹 Sources: Kaggle Tamil NLP, Sentiment Analysis datasets, and synthetic data. 🔹 Languages: Tamil, Tanglish (Tamil-English mix), and regional Tamil dialects. 🔹… See the full description on the dataset page: https://huggingface.co/datasets/ThrishaSivasakthi/Tamil-Finetuning-data.

Two datasets are included in this repository: claim matching and claim detection datasets. The collections contain data in 5 languages: Bengali, English, Hindi, Malayalam and Tamil.

The "claim detection" dataset contains textual claims from social media and fact-checking websites annotated for the "fact-check worthiness" of the claims in each message. Data points have one of the three labels of "Yes" (text contains one or more check-worthy claims), "No" and "Probably".

The "claim matching" dataset is a curated collection of pairs of textual claims from social media and fact-checking websites for the purpose of automatic and multilingual claim matching. Pairs of data have one of the four labels of "Very Similar", "Somewhat Similar", "Somewhat Dissimilar" and "Very Dissimilar".

All personally identifiable information (PII) including phone numbers, email addresses, license plate numbers and addresses have been replaced with general tags (e.g.

This dataset includes 500 hours of scripted Tamil monologue speech collected using smartphones. Each sample is transcribed with text content and metadata such as speaker ID, gender, and age. The dataset features diverse speakers from various regions, making it highly representative of real-world Tamil language use and suitable for automatic speech recognition (ASR), text-to-speech (TTS), voice activity detection (VAD), and natural language processing (NLP) tasks. Validated by leading AI companies, the dataset is designed to enhance model robustness in multilingual environments and low-resource languages. All data was collected in full compliance with global privacy regulations including GDPR, CCPA, and PIPL, ensuring ethical sourcing and responsible AI development.

Introduction

The Tamil General Domain Chat Dataset is a high-quality, text-based dataset designed to train and evaluate conversational AI, NLP models, and smart assistants in real-world Tamil usage. Collected through FutureBeeAI’s trusted crowd community, this dataset reflects natural, native-level Tamil conversations covering a broad spectrum of everyday topics.

Conversational Text Data

This dataset includes over 10000 chat transcripts, each featuring free-flowing dialogue between two native Tamil speakers. The conversations are spontaneous, context-rich, and mimic informal, real-life texting behavior.

Diversity and Domain Coverage

Conversations span a wide variety of general-domain topics to ensure comprehensive model exposure:

This diversity ensures the dataset is useful across multiple NLP and language understanding applications.

Linguistic Authenticity

Chats reflect informal, native-level Tamil usage with:

Metadata

Every chat instance is accompanied by structured metadata, which includes:

This metadata supports model filtering, demographic-specific evaluation, and more controlled fine-tuning workflows.

Data Quality Assurance

All chat records pass through a rigorous QA process to maintain consistency and accuracy:

This ensures a clean, reliable dataset ready for high-performance AI model training.

Applications

This dataset is ideal for training and evaluating a wide range of text-based AI systems:

Access a professionally curated Tamil speech dataset focused on general conversations in the medical sector. Ideal for training voice AI, speech recognition, and NLP models in healthcare.

EnTam is a sentence aligned English-Tamil bilingual corpus from some of the publicly available websites that we have collected for NLP research involving Tamil. The standard set of processing has been applied on the the raw web data before the data became available in sentence aligned English-Tamil parallel corpus suitable for various NLP tasks. The parallel corpus includes texts from bible, cinema and news domains.

203 hours of real-world Tamil speech data featuring both casual conversations and scripted monologues. All audio was recorded from native Tamil speakers across various regions, reflecting real-world linguistic and acoustic diversity. Each sample is manually transcribed and annotated with speaker ID, gender, and other metadata, making it highly suitable for automatic speech recognition (ASR), speech synthesis (TTS), speaker identification, and natural language processing (NLP) applications. The dataset has been validated by leading AI companies and is particularly valuable for training robust AI models for underrepresented languages. All data collection, processing, and usage comply strictly with global data privacy laws including GDPR, CCPA, and PIPL, ensuring legal and ethical use.

Explore our high-quality Tamil speech dataset featuring real call center conversations from the e-commerce sector. Ideal for speech recognition, NLP, and AI training applications.

High-quality Tamil speech dataset featuring Indian agent-customer finance calls, ideal for ASR, NLP, and voice AI model training.

Dataset Card for Narrinai Poems

  Dataset Summary

This dataset contains Narrinai, one of the eight anthologies (Ettuthogai) of Sangam Tamil literature.It consists of 400+ poems written by various poets, each poem categorized by number and title.
The dataset provides:

Poem Number
Title (in Tamil)
Poem Text

This can be used for NLP tasks in Tamil, such as text generation, retrieval, classification, and cultural/literary research.

  Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/TamilThagaval/Ainkurunooru.

L3Cube-IndicNews

L3Cube-IndicNews, is a multilingual text classification corpus aimed at curating a high-quality dataset for Indian regional languages, with a specific focus on news headlines and articles. We have centered our work on 11 prominent Indic languages, including Hindi, Bengali, Marathi, Telugu, Tamil, Gujarati, Kannada, Odia, Malayalam, Punjabi and English. Each of these news datasets comprises 10 or more classes of news articles. L3Cube-IndicNews offers 3 distinct… See the full description on the dataset page: https://huggingface.co/datasets/ayushbagaria17/indic-nlp.

Explore Macgence's Tamil speech dataset of Indian agent-customer call center conversations—ideal for ASR, NLP, and voice AI training applications.

The Global English Accent Conversational NLP Dataset is a comprehensive collection of validated English speech recordings sourced from native and non-native English speakers across key global regions. This dataset is designed for training Natural Language Processing models, conversational AI, Automatic Speech Recognition (ASR), and linguistic research, with a focus on regional accent variation.

Regions and Covered Countries with Primary Spoken Languages:

Africa: South Africa (English, Zulu, Afrikaans, Xhosa) Nigeria (English, Yoruba, Igbo, Hausa) Kenya (English, Swahili) Ghana (English, Twi, Ewe, Ga) Uganda (English, Luganda) Ethiopia (English, Amharic, Oromo)

Central & South America: Mexico (Spanish, English as a second language) Guatemala (Spanish, K'iche', English) El Salvador (Spanish, English) Costa Rica (Spanish, English in Caribbean regions) Colombia (Spanish, English in urban centers) Dominican Republic (Spanish, English in tourist zones) Brazil (Portuguese, English in urban areas) Argentina (Spanish, English among educated speakers)

Southeast Asia & South Asia: Philippines (Filipino, English) Vietnam (Vietnamese, English) Malaysia (Malay, English, Mandarin) Indonesia (Indonesian, Javanese, English) Singapore (English, Mandarin, Malay, Tamil) India (Hindi, English, Bengali, Tamil) Pakistan (Urdu, English, Punjabi)

Europe: United Kingdom (English) Ireland (English, Irish) Germany (German, English) France (French, English) Spain (Spanish, Catalan, English) Italy (Italian, English) Portugal (Portuguese, English)

Oceania: Australia (English) New Zealand (English, Māori) Fiji (English, Fijian) North America: United States (English, Spanish) Canada (English, French)

Dataset Attributes: - Conversational English with natural accent variation - Global coverage with balanced male/female speakers - Rich speaker metadata: age, gender, country, city - Average audio length of ~30 minutes per participant - All samples manually validated for accuracy - Structured format suitable for machine learning and AI applications

Best suited for: - NLP model training and evaluation - Multilingual ASR system development - Voice assistant and chatbot design - Accent recognition research - Voice synthesis and TTS modeling

This dataset ensures global linguistic diversity and delivers high-quality audio for AI developers, researchers, and enterprises working on voice-based applications.

Explore authentic Tamil call center speech data for banking, featuring Indian agents and customers. Curated by Macgence for voice AI and NLP projects.

Please cite this paper when using this dataset: N. Thakur, “Mpox narrative on Instagram: A labeled multilingual dataset of Instagram posts on mpox for sentiment, hate speech, and anxiety analysis,” arXiv [cs.LG], 2024, URL: https://arxiv.org/abs/2409.05292Abstract: The world is currently experiencing an outbreak of mpox, which has been declared a Public Health Emergency of International Concern by WHO. During recent virus outbreaks, social media platforms have played a crucial role in keeping the global population informed and updated regarding various aspects of the outbreaks. As a result, in the last few years, researchers from different disciplines have focused on the development of social media datasets focusing on different virus outbreaks. No prior work in this field has focused on the development of a dataset of Instagram posts about the mpox outbreak. The work presented in this paper (stated above) aims to address this research gap. It presents this multilingual dataset of 60,127 Instagram posts about mpox, published between July 23, 2022, and September 5, 2024. This dataset contains Instagram posts about mpox in 52 languages.For each of these posts, the Post ID, Post Description, Date of publication, language, and translated version of the post (translation to English was performed using the Google Translate API) are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis, hate speech detection, and anxiety or stress detection were also performed. This process included classifying each post intoone of the fine-grain sentiment classes, i.e., fear, surprise, joy, sadness, anger, disgust, or neutralhate or not hateanxiety/stress detected or no anxiety/stress detected.These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for sentiment, hate speech, and anxiety or stress detection, as well as for other applications.The 52 distinct languages in which Instagram posts are present in the dataset are English, Portuguese, Indonesian, Spanish, Korean, French, Hindi, Finnish, Turkish, Italian, German, Tamil, Urdu, Thai, Arabic, Persian, Tagalog, Dutch, Catalan, Bengali, Marathi, Malayalam, Swahili, Afrikaans, Panjabi, Gujarati, Somali, Lithuanian, Norwegian, Estonian, Swedish, Telugu, Russian, Danish, Slovak, Japanese, Kannada, Polish, Vietnamese, Hebrew, Romanian, Nepali, Czech, Modern Greek, Albanian, Croatian, Slovenian, Bulgarian, Ukrainian, Welsh, Hungarian, and Latvian.The following is a description of the attributes present in this dataset:Post ID: Unique ID of each Instagram postPost Description: Complete description of each post in the language in which it was originally publishedDate: Date of publication in MM/DD/YYYY formatLanguage: Language of the post as detected using the Google Translate APITranslated Post Description: Translated version of the post description. All posts which were not in English were translated into English using the Google Translate API. No language translation was performed for English posts.Sentiment: Results of sentiment analysis (using the preprocessed version of the translated Post Description) where each post was classified into one of the sentiment classes: fear, surprise, joy, sadness, anger, disgust, and neutralHate: Results of hate speech detection (using the preprocessed version of the translated Post Description) where each post was classified as hate or not hateAnxiety or Stress: Results of anxiety or stress detection (using the preprocessed version of the translated Post Description) where each post was classified as stress/anxiety detected or no stress/anxiety detected.All the Instagram posts that were collected during this data mining process to develop this dataset were publicly available on Instagram and did not require a user to log in to Instagram to view the same (at the time of writing this paper).

Thirukkural Dataset This dataset contains the full Thirukkural (1330 couplets) in Tamil along with English translations and explanations. It is structured in a way that is easy to use for developers, researchers, and AI projects, especially for NLP, chatbots, or literature analysis. Dataset Structure Each record in the dataset contains the following fields: Field Description verse_number Numeric index of the verse (1–1330) tamil_kural The original Tamil Thirukkural verse english_verse English… See the full description on the dataset page: https://huggingface.co/datasets/TamilThagaval/thirukkural-dataset.