Back to Courses
DevelopmentIntermediate32 lessons14–18 hours
Voice AI: Build Voice-Powered Applications
Build voice-powered AI applications. Text-to-speech, speech-to-text, voice cloning, real-time voice agents, and conversational AI interfaces.
What You'll Learn
Integrate text-to-speech APIs from ElevenLabs, PlayHT, and OpenAI
Build speech-to-text transcription with Whisper, Deepgram, and AssemblyAI
Understand voice cloning ethics, consent, and responsible implementation
Create voice-first AI agents that hold natural conversations
Handle real-time audio streaming for low-latency voice interactions
Integrate voice AI with telephony systems using Twilio and Vapi
Build multilingual voice applications with language detection and translation
Deploy production voice pipelines with monitoring and fallback handling
Outcomes
- Build voice agents that handle real conversations
- Integrate ElevenLabs, Whisper, and Deepgram into applications
- Create text-to-speech and speech-to-text pipelines for production
- Deploy voice-powered systems with low-latency streaming
Prerequisites
- -JavaScript or Python fundamentals
- -Basic understanding of APIs
Projects You'll Build
- Build a voice-enabled AI assistant
- Create a speech-to-text transcription pipeline
- Deploy a real-time voice agent with conversation handling
Course Curriculum
Module 1: Voice AI Landscape
- 1.1The state of voice AI: capabilities, limitations, and opportunities
- 1.2Voice AI architecture: input, processing, response, and output
- 1.3Key providers compared: ElevenLabs, OpenAI, Deepgram, AssemblyAI, PlayHT
- 1.4Setting up your voice AI development environment
- 1.5Your first voice app: text in, speech out in 10 minutes
Module 2: Text-to-Speech (ElevenLabs, PlayHT, OpenAI)
- 2.1ElevenLabs API: voices, models, and generation settings
- 2.2OpenAI TTS: simple, fast, and good enough for many use cases
- 2.3PlayHT: ultra-realistic voices and emotion control
- 2.4Voice cloning: creating custom voices from audio samples
- 2.5Ethics and consent: responsible voice cloning practices
- 2.6SSML and pronunciation control for precise audio output
- 2.7Streaming audio generation for real-time applications
Module 3: Speech-to-Text (Whisper, Deepgram, AssemblyAI)
- 3.1OpenAI Whisper: local and API-based transcription
- 3.2Deepgram: real-time streaming transcription with low latency
- 3.3AssemblyAI: speaker diarization, sentiment, and topic detection
- 3.4Handling audio formats, sample rates, and noise reduction
- 3.5Real-time transcription: WebSockets and streaming pipelines
- 3.6Accuracy optimization: custom vocabulary and language models
Module 4: Voice Agents & Conversation
- 4.1Voice agent architecture: listen, think, speak loop
- 4.2Turn-taking and interruption handling in voice conversations
- 4.3Emotion detection and adaptive response tone
- 4.4Building a voice-powered customer service agent
- 4.5Telephony integration with Twilio and Vapi
- 4.6Latency optimization: reducing time-to-first-byte for voice responses
- 4.7Context management across voice conversation turns
Module 5: Production Voice Systems
- 5.1End-to-end voice pipeline architecture
- 5.2Audio quality monitoring and fallback strategies
- 5.3Multilingual voice apps: language detection and translation
- 5.4Cost management: optimizing API usage and caching common responses
- 5.5Accessibility considerations for voice-first interfaces
- 5.6Scaling voice systems: concurrent sessions and load balancing
- 5.7The future of voice AI: real-time translation, emotional AI, and beyond
AI isn't slowing down.
Neither should you.
Every week you wait, the gap widens. The people who invest in learning AI now will be the ones leading teams, building companies, and staying ahead of the curve. This is your moment — don't let it pass.