Gowhar Ganie

Key Skills

Software

Appen

CVAT

Labelbox

Label Studio

OneForma

Remotasks

Scale AI

Telus

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Task Types

Data Collection

Prompt Response Writing SFT

RLHF

Text Generation

Translation Localization

Freelancer Overview

I have experience with all types of annotation; my skills are based on image, text, audio, etc., with a focus on precision, consistency, and efficiency. Having trained models across various domains, including NLP, computer vision, and more, deep learning, and other supervised models, I have independently developed projects, which have resulted in a near, human-like performance on the most diverse tasks by accurate classification and prediction. This expertise is complemented by my meticulous attention to detail and strong grasp of annotation stipulations, allowing me to maintain data integrity while making scalability workflow improvements. In my previous experience, I worked closely with AI researchers, data scientists, and engineers to ensure the quality of the datasets, identify edge cases, and, ultimately, make the model training process more robust. Whether this means leading teams of annotators or physically dealing with really complex datasets, I take a structured, analytical approach to ensure that the AI systems are learning from the information.

Labeling Experience

Speech-to-Text Annotation for Multilingual ASR Model

LabelboxAudioEntity Ner ClassificationClassification

Annotated multilingual audio recordings to train a speech-to-text AI model for automatic transcription and voice command recognition. Tasks included precise transcription, speaker diarization (identifying different speakers in a conversation), and labeling emotions to improve contextual understanding. Maintained high accuracy standards by following phonetic transcription guidelines and conducting inter-annotator agreement (IAA) checks. This dataset helped enhance ASR models used in virtual assistants, call center automation, and accessibility tools.

Annotated multilingual audio recordings to train a speech-to-text AI model for automatic transcription and voice command recognition. Tasks included precise transcription, speaker diarization (identifying different speakers in a conversation), and labeling emotions to improve contextual understanding. Maintained high accuracy standards by following phonetic transcription guidelines and conducting inter-annotator agreement (IAA) checks. This dataset helped enhance ASR models used in virtual assistants, call center automation, and accessibility tools.

2024 - 2024

Transcription and Translation Specialist

Label StudioAudioTranslation Localization

Worked on a large-scale AI training project involving transcription and translation of multilingual data to enhance machine learning models. Tasks included: Transcribing audio data with high accuracy Translating and localizing text for various languages Annotating text for AI fine-tuning and RLHF Ensuring consistency, cultural relevance, and adherence to project-specific guidelines This project required strict quality control measures to maintain accuracy and fluency, contributing to improved AI language models.

Worked on a large-scale AI training project involving transcription and translation of multilingual data to enhance machine learning models. Tasks included: Transcribing audio data with high accuracy Translating and localizing text for various languages Annotating text for AI fine-tuning and RLHF Ensuring consistency, cultural relevance, and adherence to project-specific guidelines This project required strict quality control measures to maintain accuracy and fluency, contributing to improved AI language models.

2023 - 2024

Action Recognition & Object Tracking for Surveillance AI

CVATVideoBounding BoxObject Detection

Labeled video data for an AI-powered surveillance system, annotating objects (e.g., people, vehicles, and suspicious items) and tracking movements across multiple frames. The project involved identifying specific human actions (e.g., running, falling, loitering) for real-time threat detection. Ensured high annotation accuracy through frame-by-frame quality checks and motion prediction techniques. This dataset contributed to improving AI models for automated security monitoring and anomaly detection in public spaces.

Labeled video data for an AI-powered surveillance system, annotating objects (e.g., people, vehicles, and suspicious items) and tracking movements across multiple frames. The project involved identifying specific human actions (e.g., running, falling, loitering) for real-time threat detection. Ensured high annotation accuracy through frame-by-frame quality checks and motion prediction techniques. This dataset contributed to improving AI models for automated security monitoring and anomaly detection in public spaces.

2023 - 2024

Large-Scale NLP Annotation for Sentiment Analysis & NER

AppenTextText GenerationText Summarization

Labeled large-scale text datasets for sentiment analysis and Named Entity Recognition (NER) to enhance AI-driven chatbots and customer support systems. Tasks included identifying and tagging named entities (e.g., organizations, locations, product names), classifying sentiments (positive, negative, neutral), and summarizing key insights from customer feedback. Ensured data consistency through strict annotation guidelines and quality assurance processes. The project aimed to improve NLP models for better text comprehension and contextual understanding.

Labeled large-scale text datasets for sentiment analysis and Named Entity Recognition (NER) to enhance AI-driven chatbots and customer support systems. Tasks included identifying and tagging named entities (e.g., organizations, locations, product names), classifying sentiments (positive, negative, neutral), and summarizing key insights from customer feedback. Ensured data consistency through strict annotation guidelines and quality assurance processes. The project aimed to improve NLP models for better text comprehension and contextual understanding.

2023 - 2023

Autonomous Vehicle Image Annotation for Object Detection

LabelboxImageBounding BoxPolygon

Annotated high-resolution images for an autonomous driving dataset, labeling vehicles, pedestrians, traffic signs, lane markings, and obstacles using bounding boxes and polygons. The project involved tracking objects across frames for motion prediction and ensuring high annotation accuracy for model training. I adhered to strict quality control measures, including inter-annotator agreement (IAA) checks, and optimized workflows for efficiency and scalability. The dataset contributed to improving real-time object detection and path planning in self-driving vehicles.

Annotated high-resolution images for an autonomous driving dataset, labeling vehicles, pedestrians, traffic signs, lane markings, and obstacles using bounding boxes and polygons. The project involved tracking objects across frames for motion prediction and ensuring high annotation accuracy for model training. I adhered to strict quality control measures, including inter-annotator agreement (IAA) checks, and optimized workflows for efficiency and scalability. The dataset contributed to improving real-time object detection and path planning in self-driving vehicles.

2023 - 2023

Education

Work History