Скачать или смотреть Speaker Diarization Annotation Sample | Audio Labeling with Overlap, Noise & Whisper Tags

#SpeakerDiarization #AudioAnnotation #DataAnnotationPortfolio #SpeechLabeling #MachineLearningData #AudioTagging #OpenSourceAnnotation #GeckoTool #SmartLabeling #AITraining #SecureAnnotation #ProfessionalAnnotator #WorkSample

Welcome to my professional data annotation portfolio!

In this video, I present a hands-on demonstration of Speaker Diarization Annotation using the open-source Gecko tool. This task showcases how I meticulously handle multi-speaker audio labeling, including:

🎙️ Speaker Identification
🎧 Whisper and Noise Differentiation
⏱️ Accurate Timestamp Segmentation
🔊 Overlapping Speech Tagging
👂 Silence Detection and Cutoffs

This work sample highlights not just annotation accuracy, but listening precision, labeling structure, and a smart, scalable workflow — crucial when building robust datasets for speech recognition, call analysis, virtual assistant training, and AI/ML voice models.

🧠 What’s Inside This Video:

Using Gecko (open-source) for diarization

Tagging Male_1, Female_1, etc., using real-time waveform audio cues

Capturing whispers, laughs, vocal sounds, and non-verbal noises like kissing, clapping, knocking

Marking noise segments separately when no speech is involved

Overlapping speakers handled using dual tagging

Trimming segments precisely by detecting silences - 1s

Ensuring accuracy on speech boundaries with zoom and waveform inspection

Efficient deletion, re-creation, and dragging of labeled segments for clarity

Every moment is thoughtfully marked — helping ensure high-quality input for training machine learning models in natural language understanding, speech diarization, and conversational AI.

💼 Why This Task Matters:

Speaker diarization is fundamental to:

Customer service call analysis

Meeting transcription

Multi-party podcast parsing

Voice separation in surveillance or media

This demo proves that I not only label audio — I understand speech dynamics, context, and client-specific rules. These distinctions are the difference between average and professional annotation work.

🚀 Why Hire Me?

If you’re seeking a data annotation expert who delivers high-quality work with consistency, confidentiality, and accuracy, I’m here to support your goals.

✅ Experience with audio, video, image, text, and 3D annotation
✅ Specialized in speech segmentation, emotion tagging, whisper/noise discrimination
✅ Skilled in open-source tools like Gecko, Label Studio, CVAT, Audacity, and custom clients’ tools
✅ Work independently, meet deadlines, and follow detailed instruction sets
✅ Proven track record in delivering clean datasets for training reliable AI systems

📩 Let’s work together. Contact me at:
📧 [email protected]

🛡️ Disclaimer:

This video is recorded solely as a portfolio sample. No proprietary client data, methods, tools, or approaches have been disclosed. The content is simulated to reflect the process and structure of the work I perform in real projects.

If this video relates to a project where I worked for you and you wish for it to be removed, please email me at [email protected], and I will promptly take it down. I maintain full confidentiality and data security standards — your trust and IP are always protected.