Introduction

Welcome to the South Asian Multi-Year Facial Image Dataset, thoughtfully curated to support the development of advanced facial recognition systems, biometric identification models, KYC verification tools, and other computer vision applications. This dataset is ideal for training AI models to recognize individuals over time, track facial changes, and enhance age progression capabilities.

Facial Image Data

This dataset includes over 10,000+ high-quality facial images, organized into individual participant sets, each containing:

Diversity & Representation

Image Quality & Capture Conditions

To ensure model generalization and practical usability, images in this dataset reflect real-world diversity:

Metadata

Each participant’s dataset is accompanied by rich metadata to support advanced model training and analysis, including:

Use Cases & Applications

This dataset is highly valuable for a wide range of AI and computer vision applications:

Secure & Ethical Collection

Dataset Updates & Customization

To keep pace with evolving AI needs, this dataset is regularly updated and customizable. Custom data collection options include:

The Urdu TTS Monologue Speech Dataset is a professionally curated resource built to train realistic, expressive, and production-grade text-to-speech (TTS) systems. It contains studio-recorded long-form speech by trained native Urdu voice artists, each contributing 1 to 2 hours of clean, uninterrupted monologue audio.

Unlike typical prompt-based datasets with short, isolated phrases, this collection features long-form, topic-driven monologues that mirror natural human narration. It includes content types that are directly useful for real-world applications, like audiobook-style storytelling, educational lectures, health advisories, product explainers, digital how-tos, formal announcements, and more.

All recordings are captured in professional studios using high-end equipment and under the guidance of experienced voice directors.

Recording & Audio Quality

Only clean, production-grade audio makes it into the final dataset.

Voice Artist Selection

All voice artists are native Urdu speakers with professional training or prior experience in narration. We ensure a diverse pool in terms of age, gender, and region to bring a balanced and rich vocal dataset.

Script Quality & Coverage

Scripts are not generic or repetitive. Scripts are professionally authored by domain experts to reflect real-world use cases. They avoid redundancy and include modern vocabulary, emotional range, and phonetically rich sentence structures.

Transcripts & Alignment

While the script is used during the recording, we also provide post-recording updates to ensure the transcript reflects the final spoken audio. Minor edits are made to adjust for skipped or rephrased words.

Foreign Exchange Reserves in Pakistan increased to 19607 USD Million in July from 19269.80 USD Million in June of 2025. This dataset provides the latest reported value for - Pakistan Foreign Exchange Reserves - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

External Debt in Pakistan decreased to 130310 USD Million in the first quarter of 2025 from 130921 USD Million in the fourth quarter of 2024. This dataset provides - Pakistan External Debt - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border.

"People Displaced" refers to the number of people living in displacement as of the end of each year.

"New Displacement" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once.

Contains data from IDMC's Global Internal Displacement Database.

The Global Internal Displacement Database (GIDD), maintained by the Internal Displacement Monitoring Centre (IDMC), provides comprehensive, validated annual estimates of internal displacement worldwide. It defines internally displaced persons (IDPs) in line with the 1998 Guiding Principles, as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border.

The GIDD tracks two primary metrics: "People Displaced" or population "Stock" figures, which represent the total number of people living in displacement at year-end, and "New Displacement," which counts new displacement incidents (population Flows) rather than individual people, accounting for potential multiple displacements by the same person. This dataset serves as a crucial resource for understanding long-term trends and validated displacement figures globally. For further detailed information and complete API specifications, users are encouraged to consult the official documentation at https://www.internal-displacement.org/database/api-documentation/.

"Internally displaced persons - IDPs" refers to the number of people living in displacement as of the end of each year.

"Internal displacements (New Displacements)" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once.

Gold Reserves in Pakistan remained unchanged at 64.75 Tonnes in the second quarter of 2025 from 64.75 Tonnes in the first quarter of 2025. This dataset provides - Pakistan Gold Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Introduction

Welcome to the Urdu General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Urdu speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Urdu communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Urdu speech models that understand and respond to authentic Urdu accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Urdu. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Urdu speech and language AI applications:

Whether one is in favor of private education or not, it is here to stay and there is a critical need to understand this new environment. Unfortunately, little is known about the private sector and what its growth implies for the provision of education. There are important questions we need to answer before engaging in productive debate about how education can be best provided in the Pakistani context. For instance: a. Where are private schools setting up? Are they only being established in urban areas and only for the elite? b. What is the quality of education in private sector schools? How does it compare to public schools? c. Are the poor being left out? Is the private sector creating two classes of people in Pakistan—those who can afford private education and those who cannot? d. What is the effect of private schools on government schools?