FORMATION IA
Ingénierie IA Vocale et Pipelines de Parole
Construisez des pipelines vocaux en production combinant ASR, TTS et traitement audio en temps réel.
Ce qu'elle couvre
Ce programme de niveau praticien permet aux équipes d'ingénierie de concevoir, construire et déployer des systèmes vocaux IA de bout en bout. Les participants travaillent en pratique avec les principaux moteurs ASR (Whisper, Deepgram), les fournisseurs TTS (ElevenLabs, PlayHT, Coqui) et des architectures de streaming en temps réel. Le programme couvre l'optimisation de la latence, la diarisation des locuteurs, l'éthique du clonage vocal et les patterns d'intégration pour les environnements de production. À l'issue de la formation, les équipes sont capables de concevoir et déployer des produits vocaux robustes répondant aux exigences de qualité, de performance et de conformité.
À l'issue, vous saurez
- Integrate and benchmark at least two ASR engines against a custom audio dataset using WER and latency metrics
- Build a real-time voice pipeline with sub-500ms end-to-end latency using WebSocket streaming
- Fine-tune or prompt a TTS model to produce a consistent brand voice and evaluate output with MOS scoring
- Apply speaker diarisation and transcript post-processing to multi-speaker audio recordings
- Articulate the ethical and legal boundaries of voice cloning and implement consent-verification guardrails in a pipeline
Sujets abordés
- ASR fundamentals and engine comparison: Whisper, Deepgram, Azure Speech, AWS Transcribe
- TTS system selection and voice quality tuning: ElevenLabs, PlayHT, Coqui, XTTS
- Real-time audio streaming pipelines and WebSocket/WebRTC integration
- Speaker diarisation, punctuation restoration, and transcript post-processing
- Voice cloning: technical workflow, ethical constraints, and legal considerations
- Latency budgeting and optimisation for conversational AI use cases
- Evaluation metrics: WER, MOS, latency P95, and hallucination detection
- Deployment patterns: on-premise vs. cloud API vs. self-hosted model serving
Modalité
Delivered as a 3-4 day intensive bootcamp, available in-person or fully remote via collaborative IDE (e.g. GitHub Codespaces). Each day combines 40% concept sessions with 60% hands-on lab work. Participants receive a pre-configured cloud environment with API credits for Deepgram, ElevenLabs, and OpenAI Whisper. A capstone project — building a minimal end-to-end voice agent — is completed on the final day and reviewed by the instructor. All materials, notebooks, and reference architectures are provided and retained by participants.
Ce qui fait que ça marche
- Establishing a shared audio evaluation dataset from actual production samples before the training begins
- Assigning a clear pipeline owner per team who can maintain and iterate on the voice stack after the bootcamp
- Running a latency budget review as a standard design step for any new voice feature
- Embedding ethical review checkpoints for any voice-cloning or voice-synthesis feature into the existing development workflow
Erreurs fréquentes
- Choosing a TTS or ASR provider solely on demo quality without benchmarking against real production audio conditions (noise, accents, domain vocabulary)
- Ignoring latency budgeting early in design, leading to pipelines that are technically correct but unusable in real-time conversation
- Deploying voice-cloning features without documented consent workflows, creating legal and reputational exposure
- Underestimating the post-processing work required (punctuation, disfluency removal, diarisation) to make raw transcripts usable downstream
Quand NE PAS suivre cette formation
This bootcamp is not the right fit for teams that have not yet shipped any backend service — foundational software engineering upskilling should come first before tackling real-time audio pipeline complexity.
Fournisseurs à considérer
Sources
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