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Training Voice Activity Detection (VAD) - Mastering Real-Time Voice Detection

Ref: GLJ718
10 people max.
5500€ HT / per person
−15% from 2 people−30% from 3 people−50% from 5 people
Pay in 3 installments · +$170/day onsite · +$500 with certification exam
5 journées
distanciel

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Learning objectives

  • Master advanced VAD algorithms for professional certified applications
  • Develop optimized deep learning skills for real-time corporate VAD
  • Implement robust voice detection in extreme noise for industrial projects
  • Design VAD pipelines integrated into high-performance embedded ASR systems
  • Optimize VAD models for edge computing with 50% latency reduction in production
  • Deploy multimodal VAD with vision-audio using secure corporate protocols
  • Evaluate VAD performance with precise metrics for expert certification

The Learni story

Founded by passionate learning and innovation experts, Learni's mission is to make professional training accessible to everyone, anywhere in the world. Our team operates in major hubs — London, New York, Boston — and internationally, to support talents and organizations in upskilling.

Don't let this gap widen

Why this program matters

  • Without this upskilling, your team accumulates a technological gap that translates directly into productivity loss.

  • Organizations that don't train their talents on key topics see their competitiveness drop.

  • Every quarter without training is a gap widening with competitors who invest.

  • The cost of inaction quickly exceeds that of well-targeted training.

Allan Busi
Allan Busi

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1Voice Activity Detection: Expert Fundamentals and Advanced Features (MFCC, Spectrograms)

Dive into advanced spectral features for VAD, extract optimized MFCCs, analyze time-frequency spectrograms with Librosa, implement adaptive thresholds for non-stationary noise, test robustness on noisy speech datasets, produce initial performance reports, personalized Python practical exercises for corporate use.

Module 2Voice Activity Detection: Deep Learning Neural VAD (RNN, CNN-LSTM)

Build RNN-LSTM VAD networks in PyTorch, train on LibriSpeech augmented with noise, fine-tune hybrid CNN-RNN architectures, evaluate EER/F1-score on real datasets, optimize hyperparameters with grid search, deploy GPU inference prototypes, industrial ASR use cases, deliver trained models exported to ONNX.

Module 3Voice Activity Detection: Real-Time Embedded Optimization (TensorRT, ONNX)

Accelerate real-time VAD with NVIDIA TensorRT, convert models to ONNX runtime, integrate streaming audio pipelines with WebRTC, test latency <10ms on edge devices, handle concurrent multi-streams, debug bottlenecks with profiling tools, Raspberry Pi embedded exercises, produce comparative deployment benchmarks for production.

Module 4Voice Activity Detection: Multimodal Vision-Audio VAD (Feature Fusion)

Fuse audio VAD with lip detection using OpenCV, implement visual features from AV-HuBERT, train multimodal models with PyTorch Lightning, test robustness to occlusions/variable lighting, integrate real-time video call APIs, evaluate 20% accuracy gains, corporate Zoom/Teams use cases, deliver interactive fused demos.

Module 5Voice Activity Detection: Expert Projects and Scalable Deployment (Docker, Kubernetes)

Develop end-to-end VAD projects on AWS/GCP cloud, containerize with scalable Docker Kubernetes, monitor performance with Prometheus/Grafana, secure APIs with HTTPS OAuth, test loads of 1000+ streams, pitch business solutions for real cases, certify skills with portfolio, post-training support for corporate deployment.

Evaluation method

  • VAD deep learning technical quizzes with 80% success rate
  • Final multimodal real-time VAD project evaluated by experts
  • Qualiopi certification validating VAD skills

Learning method

  • 70% hands-on intensive VAD coding workshops
  • Real industrial audio enterprise cases
  • Individual mentoring with 1/10 trainer ratio
  • Lifetime resources: codes, datasets, tools

Methods, materials and delivery

The Training Voice Activity Detection (VAD) - Mastering Real-Time Voice Detection program is delivered onsite or remote (blended-learning, e-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.

The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.

Equipment required

For the smooth delivery of the Training Voice Activity Detection (VAD) - Mastering Real-Time Voice Detection program, the following equipment is required:

  • Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
  • Training environments installed on workstations or accessible online
  • Course materials, hands-on exercises and complementary resources
  • Post-training access to materials and educational resources

For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.

* contact us for remote delivery feasibility** ratio varies depending on the program

Skills assessment methods

Assessment of skills acquired during the Training Voice Activity Detection (VAD) - Mastering Real-Time Voice Detection program is performed through:

  • During training: case studies, hands-on labs and professional scenarios
  • End of training: self-assessment questionnaire and skills evaluation by the trainer
  • After training: completion certificate detailing acquired skills

Program accessibility

Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.

Enrollment terms and lead times

Learni programs are available inter-company and intra-company, onsite or remote. Enrollments are possible up to 48 business hours before the program starts. Our programs are eligible for corporate funding paths. Contact us to discuss your training project and funding options.

Verified reviews

What our learners

4.9 · +100 verified reviews
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
Read all reviews
Our method

Training quality, guaranteed at every step

Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.

Step 1

Rigorous trainer selection

Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.

  • Triple validation: technical, pedagogical, sectoral.
  • Minimum rating 4.8/5 over the last 12 sessions.
Step 2

You meet the trainer beforehand

30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.

  • Live briefing on goals and team context.
  • Veto right — we swap the trainer for free if needed.
Step 3

Content tailored to your context

No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.

  • Hands-on cases drawn from your stack and projects.
  • Program co-written then validated by your team.
Step 4

Continuous quality follow-up

Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.

  • NPS, knowledge quizzes and skills self-assessment.
  • Satisfaction guarantee: fully satisfied or free rework.

A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.

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