Voice and Multimodal AI for Developers

Class Duration

14 hours of live training delivered over 2-3 days to accommodate your scheduling needs.

Student Prerequisites

Professional software development experience in TypeScript or Python
Familiarity with REST APIs and async programming

Target Audience

Software engineers adding voice, image, or document understanding capabilities to applications. Relevant for teams building voice assistants, accessibility features, document processing pipelines, image-aware support tools, or multimodal AI features in web and mobile applications.

Description

This course covers the practical implementation of voice and multimodal AI features in production applications. We cover real-time voice APIs (OpenAI Realtime API, speech-to-text, text-to-speech), vision capabilities (image understanding with Claude Opus/Sonnet 4.x and GPT-5.x, screenshot analysis, diagram interpretation), document understanding (PDF and image extraction, structured extraction from documents), and audio input/output pipelines. Labs build working multimodal features in TypeScript and Python.

Learning Outcomes

Integrate real-time speech-to-text and text-to-speech APIs into a web application.
Use the OpenAI Realtime API (or equivalent) for low-latency voice conversation features.
Submit images to vision-capable models and extract structured information.
Build a document understanding pipeline that extracts structured data from PDFs and scanned documents.
Handle the unique latency, error, and UX considerations of voice and multimodal features.
Apply cost and privacy considerations specific to voice and image data.

Training Materials

Comprehensive courseware is distributed online at the start of class. All students receive a downloadable MP4 recording of the training.

Software Requirements

Node.js 20+ or Python 3.12+, API keys for OpenAI and/or Anthropic, and a microphone for voice labs.

Training Topics

Speech-to-Text and Text-to-Speech

Whisper and OpenAI transcription API
Real-time vs. batch transcription tradeoffs
Speaker diarization and timestamping
Text-to-speech options: ElevenLabs, OpenAI TTS, Cartesia
Voice selection, speed, and tone controls
Streaming TTS for low-latency UX

OpenAI Realtime API for Voice

Realtime API architecture: WebSockets and audio streams
Session configuration, voice activity detection, and interruption handling
Tool calling within a realtime voice session
Integrating realtime voice into a web app
Latency optimization and fallback strategies
Cost management for long-running voice sessions

Vision: Image Understanding

Submitting images to Claude and GPT vision-capable models
Use cases: screenshot analysis, diagram interpretation, UI review
Structured extraction from images with JSON output
Bounding-box and region-of-interest extraction
Token and cost considerations for vision inputs

Document Understanding

PDF and image-based document processing
Extracting structured data from forms and tables
Multi-page document handling and chunking
Combining vision and text extraction pipelines
OCR fallback strategies for scanned documents

Multimodal Application Patterns

Combining voice + vision in a single session
Audio + image input for accessibility features
Multimodal RAG: images in the retrieval pipeline
Multimodal embeddings and cross-modal search
Caching and cost management for multimodal inputs

Real-Time Streaming and UX

Audio buffering and partial-transcript UX
Barge-in and interruption design
Latency budgets for conversational features
Mobile and browser audio capture pitfalls

Reliability and Error Handling

Handling transient transcription errors
Fallback paths when realtime sessions disconnect
Recovery patterns for long-running voice agents
Observability for voice and multimodal pipelines

Privacy and Compliance for Voice and Image Data

Data retention policies for voice recordings
PII in images: detection and redaction
Consent and disclosure requirements (including dual-party-consent jurisdictions)
Data residency for voice and image processing
Air-gapped or self-hosted alternatives for regulated data

Workshop

Voice conversation feature implementation lab (Realtime API)
Document extraction pipeline lab
Multimodal RAG mini-project
Q&A session