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Seif Ehab

Seif Ehab

Detail-oriented English data annotator | Text, audio & image labeling

Egypt flagcairo, Egypt
$10.00/hrEntry LevelData Annotation TechOther

Key Skills

Software

Data Annotation TechData Annotation Tech
Other

Top Subject Matter

No subject matter listed

Top Data Types

AudioAudio
ImageImage
VideoVideo

Top Task Types

Audio Recording
Data Collection
Diagnosis
Text Generation

Freelancer Overview

I have hands-on experience in data annotation and dataset preparation across multiple domains, including medical imaging, symptom-based diagnostic data, and English audio commands. As part of my academic projects, I contributed to creating and annotating datasets, structuring medical symptom records, and labeling speech commands for supervised learning. These experiences gave me a strong foundation in text, audio, and image labeling workflows, as well as a deep appreciation for accuracy and consistency in training AI models. In addition to technical exposure, I bring proven remote work experience as a customer service representative with Verizon and as an online coding instructor at BrightChamps. Both roles strengthened my skills in English communication, attention to detail, and remote collaboration tools, making me well-prepared to follow detailed labeling guidelines and deliver high-quality results in distributed teams.

Entry LevelArabicEnglish

Labeling Experience

Speech Command Dataset Annotation for Audio Classification

OtherAudioClassificationData Collection
Created a custom dataset of English words and spoken commands to train a speech recognition system. Collected, annotated, and organized audio samples with clear labeling for supervised learning. Applied preprocessing techniques (MFCC feature extraction) to ensure consistent quality across the dataset. The project included labeling and categorizing hundreds of audio samples for training, validation, and testing, with a focus on accuracy, consistency, and reproducibility.

Created a custom dataset of English words and spoken commands to train a speech recognition system. Collected, annotated, and organized audio samples with clear labeling for supervised learning. Applied preprocessing techniques (MFCC feature extraction) to ensure consistent quality across the dataset. The project included labeling and categorizing hundreds of audio samples for training, validation, and testing, with a focus on accuracy, consistency, and reproducibility.

2024 - 2024

Education

T

The Egyptian Russian University

Bachelor of Science, Artificial Intelligence

Bachelor of Science
2022

Work History

V

Verizon

Customer Service Representative

Cairo
2022 - 2023