muhammad-khubaib-ahmad

Introduction

Muhammad Khubaib Ahmad is an AI/ML Engineer and Applied Researcher in Speech, NLP, and Data-Centric Systems, based in Multan, Punjab, Pakistan. His work focuses on speech AI, vocal health analysis, data-centric Roman Urdu NLP, and the engineering of intelligent decision support systems.

His research combines deep learning, contrastive representation learning, and applied machine learning with end-to-end system design. In addition to research contributions, he has developed multiple open-source libraries and deployed production-level AI applications across healthcare, agriculture, and data analytics domains.

Professional Summary

Muhammad Khubaib Ahmad is an AI/ML engineer and speech AI researcher whose work lies at the intersection of representation learning, vocal health analysis, and data-centric natural language processing. His primary research focus includes speech embeddings, speaker-invariant modeling, Roman Urdu NLP, and intelligent forecasting-based decision support systems.

He has authored multiple research preprints published on Zenodo and Hugging Face with assigned DOIs, and has released open-source Python libraries for vocal biometrics, vocal fatigue scoring, and synthetic data generation. His research emphasizes reproducibility, benchmarking, and real-world applicability through deployed AI systems and APIs.

Alongside research, he has engineered multi-agent data systems, MLOps pipelines, forecasting platforms, and knowledge-based AI applications. His broader technical work also includes generative AI, multi-agent systems, reinforcement learning, and end-to-end deployment of machine learning models for real-world use cases.

Research and publications

Muhammad Khubaib Ahmad’s research spans speech AI, vocal health modeling, Roman Urdu NLP, and intelligent decision support systems. His work emphasizes contrastive learning, data-centric methodologies, and system-level engineering for applied AI.

• Modeling Vocal Fatigue as Embedding-Space Deviation Using Contrastively Trained ECAPA-TDNNs

Ahmad, M. K. (2026). Modeling Vocal Fatigue as Embedding-Space Deviation Using Contrastively Trained ECAPA-TDNNs. Zenodo. DOI: https://doi.org/10.5281/zenodo.18366305

This work introduces a novel formulation of vocal fatigue detection using embedding-space deviation learned through supervised contrastive training on ECAPA-TDNN architectures. The proposed ECAPA-TDNN-VHE model demonstrates significant performance improvements over baseline ECAPA-TDNN models and provides a research-oriented framework for objective vocal fatigue scoring.

• Data-Centric Roman Urdu NLP: High-Quality Dataset Curation, Privacy-Preserving Embeddings, and State-of-the-Art Model Benchmarking

Ahmad, M. K. (2025). Data-Centric Roman Urdu NLP: High-Quality Dataset Curation, Privacy-Preserving Embeddings, and State-of-the-Art Model Benchmarking. Zenodo. DOI: https://doi.org/10.5281/zenodo.18080524

This study proposes a data-centric framework for Roman Urdu NLP, emphasizing high-quality dataset curation, privacy-preserving embedding generation, and benchmarking of modern transformer-based models for sentiment analysis and downstream NLP tasks.

• Forecast-Based Decision Support System to Manage Mango Malformation Disease: A Smart Farming Approach

Ahmad, M. K. A., & Mangana, A. A. M. (2025). Forecast-Based Decision Support System to Manage Mango Malformation Disease: A Smart Farming Approach. Zenodo. DOI: https://doi.org/10.5281/zenodo.16090477

This research presents an intelligent forecasting-based decision support system for managing mango malformation disease. The work focuses on system engineering, predictive modeling, and real-world agricultural deployment, integrating forecasting techniques with actionable decision-making workflows for smart farming.

Open-source Models

• ECAPA-TDNN-VHE (Vocal Health Encoder)

Ahmad, M. K. (2026). ECAPA-TDNN-VHE. Hugging Face Model Repository. DOI: 10.57967/hf/7648 https://huggingface.co/Khubaib01/ECAPA-TDNN-VHE

ECAPA-TDNN-VHE is a research-grade vocal health encoder trained using supervised contrastive learning to separate pathological and healthy vocal states while suppressing speaker identity. The model forms the backbone of objective vocal fatigue scoring in applied clinical-oriented speech AI systems.

• Roman Urdu Sentiment XLM-R Model

Ahmad, M. K. (2025). roman-urdu-sentiment-xlm-r. Hugging Face Model Repository. DOI: 10.57967/hf/7130 https://huggingface.co/Khubaib01/roman-urdu-sentiment-xlm-r

A transformer-based sentiment classification model trained on curated Roman Urdu datasets, providing benchmark performance for Roman Urdu sentiment analysis tasks.

Open-source python libraries

Muhammad Khubaib Ahmad maintains a suite of open-source Python libraries that serve real needs in synthetic data generation, speech biometrics, and research-oriented vocal feature processing. Each library includes clear Python usage examples and installation instructions so developers and researchers can adopt them directly.

• faker-pk

Purpose faker-pk is a synthetic data generation library tailored for Pakistani context data. It produces realistic names, CNIC numbers, phone numbers, addresses, company info, salaries, and demographic fields. It also integrates as a provider with the popular Faker ecosystem for broader usage.

Installation

pip install faker-pk

Basic Usage

from faker_pk import FakerPK

fake = FakerPK()

# generate single values
print(fake.male_name())        # e.g., "Ali Raza"
print(fake.cnic())              # e.g., "35201-6543210-7"
print(fake.phone_number())      # e.g., "+923001234567"
print(fake.full_address())      # e.g., "House No. 45, Street 10, Lahore, Punjab, 54000"
print(fake.company_name())      # e.g., "TechWorks Pvt Ltd"

Generate Multiple Records

fake = FakerPK()

names = fake.male_name(5)
print(names)   # list of 5 male names

cities = fake.city(3)
print(cities)  # e.g., ['Lahore', 'Islamabad', 'Multan']

Faker Provider Integration

You can register faker-pk as a provider for the standard Faker package:

from faker import Faker
from faker_pk import FakerPKProvider

fake = Faker()
fake.add_provider(FakerPKProvider)

print(fake.pk_male_name())      # e.g., "Usman Raza"
print(fake.pk_cnic())           # e.g., "37405-1234567-8"
print(fake.pk_full_address())   # e.g., full Pakistani address

This integration allows seamless blending with other Faker providers for mixed regional and generic synthetic data.

Use Cases

• Populate SQL/NoSQL databases for testing

• Generate ML training and validation datasets

• Create demo or staging environments with realistic lookalike Pakistani fields

• vocalid

Purpose

vocalid is a lightweight Python library for voice-based biometric verification. It enables training voice identification models and performing verification on audio files or live streams. It’s designed for developers building speaker authentication in apps and research on speaker embeddings.

Installation

pip install vocalid

Prepare Dataset Structure Your dataset folder should look like:

dataset/ ├── my_voice/ # positive samples (target speaker) │ sample1.wav │ sample2.wav └── other_voices/ # negative samples (others) voice1.wav voice2.wav

Training a Verification Model

from vocalid.trainer import VoiceTrainer
import glob

pos_files = glob.glob("dataset/my_voice/*.wav")
neg_files = glob.glob("dataset/other_voices/*.wav")

trainer = VoiceTrainer()
trainer.train(pos_files, neg_files, save_path="voice_model.pkl")

Evaluate the Model

trainer.load("voice_model.pkl")

test_pos = glob.glob("dataset/my_voice_test/*.wav")
test_neg = glob.glob("dataset/other_voices_test/*.wav")

metrics = trainer.evaluate(test_pos, test_neg)
print("Accuracy:", metrics["accuracy"])
print(metrics["report"])

Verification Examples

from vocalid.verifier import VoiceVerifier

verifier = VoiceVerifier("voice_model.pkl")
ok, score = verifier.verify_file("unknown.wav")
print("Verified:", ok, "Score:", score)

CLI Workflow

# Train
vocalid train --positive my_voice --negative others --output model.pkl

# Evaluate
vocalid evaluate --model model.pkl --positive my_voice_test --negative others_test

# Verify a file
vocalid verify sample.wav --model model.pkl

vocalid supports both batch evaluation and live audio verification on systems with a microphone.

• auralis-vfs

Purpose

auralis-vfs is a Python library focused on research-level vocal feature extraction and vocal fatigue scoring. It is purpose-built for objective analysis of speech signals using the ECAPA-TDNN-VHE models and related pipelines developed by Muhammad Khubaib Ahmad. It supports preprocessing, embedding extraction, and plugin-ready components for downstream classification.

Installation

pip install auralis-vfs

Usage

Scoring a waveform

import numpy as np
from auralis.scorer import score_waveform

# Generate fake waveform (1 second of audio at 16kHz)
waveform = np.random.randn(16000).astype("float32")

score = score_waveform(waveform)
print(f"Vocal Fatigue Score: {score:.2f}")

Scoring an audio file

from auralis.scorer import score_audio

audio_path = "path/to/speech_sample.wav"
score = score_audio(audio_path)
print(f"Vocal Fatigue Score: {score:.2f}")

Audio Validation

Supported formats: .wav, .mp3, .m4a Duration: 5–10 seconds recommended Scores range from 0 (no fatigue) to 100 (severe fatigue).

This library abstracts low-level feature extraction and provides modular APIs suitable for research experiments or integration into higher-level systems like REST APIs or dashboards.

Open-source datasets

Muhammad Khubaib Ahmad has released datasets designed to advance research in low-resource language processing and embedded representation learning. These datasets are structured, documented, and designed for integration with modern ML frameworks.

• Roman Urdu Sentiment Embeddings Dataset

Overview The Roman Urdu Sentiment Embeddings Dataset is a structured dataset of sentiment annotations and corresponding embedding vectors for Roman Urdu text. It was created with an emphasis on data quality, balanced sentiment distribution, and support for benchmarking modern NLP architectures in low-resource languages.

Objectives

Provide a high-quality benchmark dataset for Roman Urdu sentiment tasks.

Enable research on representation learning for under-served languages.

Support embedding-based analysis pipelines and downstream evaluation.

Domain: Low resource NLP

Repository: Khubaib01/roman-urdu-sentiment-embeddings

DOI: 10.57967/hf/7371

AI Systems and Deployed Applications

Muhammad Khubaib Ahmad has built and deployed a portfolio of full-stack AI systems, ranging from research-oriented tools to production-grade applications used for forecasting, analytics, vocal health, and NLP services.

Real Estate Price Intelligence Platform – Predicts property prices and provides interactive dashboards with SQL query support.

Knowledge Base Chatbots – Conversational AI for domain-specific knowledge retrieval and API integration.

Sales Forecasting System – Automated forecasting using ARIMA/SARIMAX models with dashboards.

Vocalytics – Speech analysis suite for vocal energy, fatigue, and spike detection with automated reports.

Roman Urdu Sentiment Demo – Hugging Face Space for live sentiment analysis of Roman Urdu text.

Auralis MLOps API – REST endpoints for vocal fatigue scoring with integration-ready pipelines.

Confidetect – Desktop application for confidence analysis and session-level reporting.

Relationship Chat Analyzer – Quantifies emotional contribution and communication balance from chat logs.

Generative & Vision AI Tools – Includes local LLM inference, LoRA fine-tuning, sketch-to-image apps, ControlNet image generation, video captioning, and live camera-based violence detection.

Technical Skills

Muhammad Khubaib Ahmad possesses a broad set of technical skills spanning AI research, software engineering, and applied machine learning.

Core Expertise:

Speech AI & Vocal Health Analysis

NLP (Roman Urdu) & Data-Centric AI

Machine learning, Computer Vision, Deep Learning, Representation Learning, Forecasting

• Data & Engineering:

SQL & data pipelines, structured & unstructured data

MLOps workflows, deployment, automated reporting

• Additional Skills

Adobe Photoshop, Premiere Pro, After Effects , Adobe Audition

Academic and Professional Background

Muhammad Khubaib Ahmad is currently pursuing a Bachelor’s degree in Artificial Intelligence (4th semester) with a CGPA of 3.97, ranking as the highest achiever in his cohort.

He has contributed to research in speech AI, vocal health analysis, Roman Urdu NLP, and intelligent forecasting systems, producing open-source libraries, datasets, and models deployed in real-world applications. His work emphasizes reproducible research, system design, and practical integration of AI solutions across domains including healthcare, agriculture, and data analytics.

Alongside his academic pursuits, he maintains a strong presence in the AI/ML community through open-source contributions, deployed applications, and Hugging Face projects, demonstrating applied expertise in both research and engineering.

Links and Profiles

GitHub: https://github.com/Khubaib8281

LinkedIn: https://www.linkedin.com/in/muhammad-khubaib-ahmad-

Hugging Face: https://huggingface.co/Khubaib01

Kaggle: https://www.kaggle.com/muhammadkhubaibahmad

Contact

Email: [email protected]

LinkedIn: https://www.linkedin.com/in/muhammad-khubaib-ahmad-