À propos de Hafsati
Je suis docteur en traitement du signal audio & IA, et j’interviens comme Lead Audio sur des chaînes audio complètes (du capteur jusqu’au cloud) : robustesse bruit/accents, latence, coûts, privacy, monitoring qualité.
J’aide les équipes Produit/Data/Tech à décider vite et bien sur des sujets voix (ASR, diarisation, audio analytics, quality monitoring) et à passer de POC à production avec une approche mesurable (métriques, protocole d’évaluation, analyse d’erreurs, roadmap).
Ce que vous gagnez : clarté + réduction du risque + plan d’exécution concret (edge/cloud, on-prem ou SaaS).
Expertise : audio ML / speech, évaluation & benchmarking, data readiness/annotation, architecture edge+cloud, MLOps/monitoring, optimisation qualité/coûts/latence.
Offres packagées:
- Audit IA Voix + Roadmap (10 jours ouvrés)
- Benchmark & Vendor Due Diligence (7 jours ouvrés)
Français
Bilingue ou natif
Arabe
Bilingue ou natif
Anglais
Capacité professionnelle complète
Accepte de travailler sur site
Paris (jusqu’à 50 km)
Expériences
- Enchanted ToolsAudio Technical Specialist and AI expertdécembre 2023 - Aujourd'hui (2 ans et 6 mois)Paris, FranceDevelop and deploy advanced audio signal processing pipelines to enhance the robot’s auditory perception (robust front-end processing, noise robustness, and real-time constraints).Build AI-driven audio capabilities including speech recognition (ASR), sound event understanding, and sound localization to support natural human–robot interaction.Implement and optimize on-device ASR using Whisper and Wav2Vec 2.0, targeting low-latency inference on edge hardware.Design and deploy Text-to-Speech (TTS) and speech transcription systems, including flow-matching-based TTS optimized for Jetson Orin / Xavier.Integrate audio services across the robot stack using gRPC-based communication, enabling reliable streaming, modularity, and scalable deployment.Optimize end-to-end performance on embedded platforms (profiling, quantization/acceleration where applicable, memory/latency trade-offs) to meet real-time requirements.Deliver end-to-end “live” conversational pipelines by integrating streaming ASR + TTS with Gemini Live, enabling low-latency interactive voice experiences.Collaborate with cross-functional robotics teams (perception, embedded, product) to ensure seamless integration, robust testing, and production readiness.Stay current with state-of-the-art speech and audio research, rapidly prototyping and transferring new methods into production.
- TUITOTechnical Referee Audio Signal Processing and AImars 2021 - novembre 2023 (2 ans et 8 mois)13600 La Ciotat, FranceBuilt real-time Voice Activity Detection (VAD) pipelines to reliably segment speech from noise, improving responsiveness and overall UX in production audio streams.Designed and integrated wake-word detection models optimized for high precision and low false-activation, ensuring robust always-on triggering across devices.Developed speech enhancement solutions using advanced signal processing and learning-based methods to reduce noise/reverberation and improve intelligibility in challenging acoustic conditions.Implemented speech separation and sound source separation techniques to isolate overlapping speakers and sources in multi-speaker, noisy environments.Developed and fine-tuned multilingual Automatic Speech Recognition (ASR) systems, leveraging deep learning architectures and large-scale dataset adaptation to boost accuracy.Built Natural Language Understanding (NLU) components for conversational systems, improving intent classification and entity extraction for reliable query interpretation.Delivered Speech-to-SQL capability: mapped spoken queries into structured SQL for voice-driven database retrieval and manipulation.Designed and deployed conversational AI agents with contextual understanding and multi-turn dialogue handling, improving naturalness and task completion.
- Sorbonne Paris North University (Université Sorbonne Paris Nord) & Université de ToulonTeaching Assistant (Signal Processing & Machine Learning / Robotics)HIGH TECHseptembre 2021 - août 2025 (3 ans et 11 mois)Paris, FranceDeliver tutorials (TD) and practical labs (TP) for Bachelor’s (L3SPI) and Master’s programs (M1 Image & Networks, Master ROC – Robotics).Teach Signal Theory and Digital Signal Processing, helping students connect mathematical foundations to real-world engineering applications.Design and deliver courses on Python fundamentals for robotics and data-driven engineering workflows.Explain and supervise supervised learning concepts: perceptrons, MLP, forward pass/loss computation, backpropagation, and optimization via gradient descent.Teach deep learning models and modern architectures: CNNs, RNNs, autoencoders, attention mechanisms, and transformers.Lead sessions on multimodal perception, combining image/audio/text data for robust perception and analysis.Adapt pedagogy to student level and learning needs, emphasizing critical thinking, structured problem-solving, and hands-on implementation.Collaborate with academic teams to develop and improve course materials and ensure learning-outcome quality.
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Formations
- Doctor of Philosophy - PhDUniversité de Rennes 12020Doctor of Philosophy - PhD
- Master's Research Signal Image System EmbArqué (SISEA)Supélec Rennes, Université de Rennes 12016Master's Research SISEA