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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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Explore the evolving role of artificial intelligence in crafting tailored educational journeys, with projections for groundbreaking advancements by April 2026.
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Artificial Intelligence training in San Francisco in October 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
Don't let this gap widen
Sans maîtrise avancée de DeepSpeech pour la reconnaissance vocale open source, 65% des intégrations applicatives échouent, générant des erreurs de transcription jusqu'à 35% sur des flux réels.
Chaque projet défaillant coûte en moyenne 15 000 € en refontes et debugging, avec des délais supplémentaires de 2 à 4 mois qui plombent les budgets IT.
Votre entreprise expose ses données sensibles à des fuites (20% des incidents vocaux liés à des implémentations imparfaites), tandis que vos concurrents captent 25% de parts de marché supplémentaires.
Chaque trimestre sans expertise accélère la perte de compétitivité, menaçant directement la pérennité business et votre ascension professionnelle.
The Maîtriser DeepSpeech : Reconnaissance Vocale avec l’IA Open Source training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Maîtriser DeepSpeech : Reconnaissance Vocale avec l’IA Open Source training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Maîtriser DeepSpeech : Reconnaissance Vocale avec l’IA Open Source training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Historique et enjeux de la reconnaissance vocale, présentation de DeepSpeech (origines, architecture, évolutions), installation sur Windows/Linux/Mac, découverte des API Python et CLI, premiers essais de transcription audio, présentation des jeux de données publics.
Comprendre le traitement du signal audio (sampling, bruit, codecs), nettoyage et annotation manuelle/automatique, création de jeux de données personnalisés, extraction des features (MFCC, spectrogrammes), découverte d’outils pratiques pour le preprocessing, exercices d’étiquetage et préparation de corpus multilingues.
Entraînement sur GPU/CPU, monitoring des étapes, hyperparamètres clés, fine-tuning sur un vocabulaire spécifique, gestion du vocabulaire et d’un dictionnaire de prononciation, intégration de DeepSpeech dans des applications Python et Node.js, mise en œuvre sur application web ou mobile, techniques d’optimisation et de déploiement en production, retours d’expérience et solutions aux problèmes fréquents.
Target audience
Développeurs, ingénieurs en IA, data scientists, professionnels souhaitant intégrer la reconnaissance vocale dans leurs applications
Prerequisites
Bonne connaissance du langage Python, bases en Machine Learning et en traitement du signal audio
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