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+# Inference Lab
+
+**Applied AI Engineering & Research — Multan, Pakistan**
+
+Inference Lab is an applied AI research and engineering organization. We develop production-grade AI systems, construct high-quality datasets for underrepresented languages, and publish reproducible research across low-resource NLP, speech intelligence, and AI deployment. Our work is end-to-end: from raw data collection and annotation through model training, evaluation, and deployment as usable software.
+
+---
+
+## Research
+
+### Low-Resource NLP
+
+**Data-Centric Roman Urdu NLP: High-Quality Dataset Curation, Privacy-Preserving Embeddings, and State-of-the-Art Model Benchmarking**
+
+A comprehensive data-centric study addressing the critical gaps in Roman Urdu NLP infrastructure. Covers rigorous dataset curation methodology, privacy-preserving embedding strategies, and systematic benchmarking of state-of-the-art models on Roman Urdu classification tasks. Establishes reproducible baselines for future work in this domain.
+
+→ [Preprint](https://doi.org/10.5281/zenodo.18080524)
+
+---
+
+**RUEmoCorp: A Large-Scale Roman Urdu Emotion Corpus with Cross-Institute Annotation Validation and State-of-the-Art Emotion Classification**
+
+Construction and release of the largest Roman Urdu emotion recognition corpus to date. Introduces a cross-institute annotation validation framework with structured annotator roles, multi-round calibration, and Inter-Annotator Agreement (IAA) measurement. Accompanies the current state-of-the-art emotion classifier for Roman Urdu.
+
+→ *Under Progress*
+
+---
+
+### Speech AI
+
+**Modeling Vocal Fatigue as Embedding-Space Deviation Using Contrastively Trained ECAPA-TDNNs**
+
+A novel approach to vocal fatigue detection that frames the problem as deviation measurement in speaker embedding space rather than direct classification. A contrastively trained ECAPA-TDNN encoder is used to capture speaker-specific vocal baselines; fatigue is quantified as geometric distance from the healthy reference embedding. Introduces the ECAPA-TDNN-VHE architecture, achieving 2.5× performance improvement over the standard ECAPA-TDNN baseline.
+
+→ [Preprint](https://doi.org/10.5281/zenodo.18366305)
+
+---
+
+**Continuous Vocal Load Monitoring in Professional Voice Users: Development and Occupational Validation of an Automated Assessment System**
+
+A complete occupational health monitoring system for professional voice users — teachers, call center operators, broadcasters, and clinical staff. Addresses the gap between laboratory vocal fatigue research and deployable real-world monitoring tools. Validated against occupational use conditions with a focus on practical deployment in professional environments.
+
+→ *Under Review — Journal of Voice*
+
+---
+
+## Datasets
+
+**RUEmoCorp** — Largest curated Roman Urdu Emotion Corpus
+
+Multi-class emotion recognition corpus for Roman Urdu, constructed with structured annotation pipelines, cross-institute validation, and rigorous quality control. Supports research in low-resource affective computing and multilingual NLP.
+
+[HuggingFace](#) · [Harvard Dataverse](https://doi.org/10.7910/DVN/BPWHOZ)
+
+---
+
+**Roman Urdu Sentiment Corpus** — Largest curated Roman Urdu Sentiment Corpus
+
+Large-scale sentiment corpus for Roman Urdu, released with full documentation of collection methodology, annotation schema, and inter-annotator agreement statistics. Serves as the benchmark dataset for Roman Urdu sentiment classification.
+
+[HuggingFace](https://huggingface.co/datasets/Khubaib01/RomanUrdu-NLP-Sentiment-Corpus) · [Harvard Dataverse](https://doi.org/10.7910/DVN/TMXDCL)
+
+---
+
+## Models
+
+**ECAPA-TDNN-VHE** — Vocal Health Encoder
+
+Custom ECAPA-TDNN architecture trained contrastively for vocal health assessment. Encodes speaker vocal characteristics into a health-sensitive embedding space. Achieves 2.5× performance improvement over the standard ECAPA-TDNN baseline on vocal fatigue detection benchmarks.
+
+[HuggingFace](https://huggingface.co/Khubaib01/ECAPA-TDNN-VHE)
+
+---
+
+**Roman Urdu Emotion Classifier** — Current State of the Art
+
+XLM-RoBERTa fine-tuned on RUEmoCorp for multi-class Roman Urdu emotion recognition. Macro F1: 0.9896. The highest-performing publicly available model for this task.
+
+[HuggingFace](https://huggingface.co/Khubaib01/roman-urdu-emotion-xlmr-v2)
+
+---
+
+**Roman Urdu Sentiment Classifier** — Current State of the Art
+
+XLM-RoBERTa fine-tuned on the Roman Urdu Sentiment Corpus. The highest-performing publicly available model for Roman Urdu sentiment classification.
+
+[HuggingFace](https://huggingface.co/Khubaib01/roman-urdu-sentiment-xlmr)
+
+---
+
+## Software
+
+**VoiceMonitor**
+Python library for continuous vocal load monitoring. Designed for integration into occupational health workflows, real-time audio pipelines, and professional voice user monitoring systems.
+
+**Auralis VFS**
+Vocal fatigue scoring library. Provides a programmable interface for fatigue quantification using the ECAPA-TDNN-VHE encoder. Designed for clinical and occupational deployment scenarios.
+
+**VocalID**
+Voice biometrics library for speaker verification and identification. Built for security-sensitive applications requiring speaker authentication from raw audio.
+
+**faker-pk**
+Localized synthetic data generation library for Pakistan. Generates realistic dummy data — names, addresses, CNICs, phone numbers, and institutional identifiers — for database seeding, system testing, and privacy-safe development workflows.
+
+---
+
+## Standards
+
+Every release from Inference Lab adheres to the following:
+
+- Reproducible training and evaluation pipelines with public code
+- Rigorous evaluation reporting — macro F1, per-class metrics, confidence intervals where applicable
+- Documented data collection, annotation methodology, and IAA statistics
+- Deployable inference code alongside model weights
+- Honest documentation of limitations and failure cases
+- Archival publication on Harvard Dataverse with permanent DOIs for all datasets
+
+---
+
+## Contact
+
+**[Muhammad Khubaib Ahmad](https://www.linkedin.com/in/muhammad-khubaib-ahmad-)** — Founder, Lead Researcher & Engineer
+
+Gmail: inferencelab.ai@gmail.com 
+
+[GitHub](https://github.com/Khubaib01) · Multan, Punjab, Pakistan