Voice AI: Build Voice-Powered Applications
Build voice agents that handle real conversations — phone bots, voice assistants, transcription pipelines. ElevenLabs, Whisper, Deepgram, Twilio integration.
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What's Included
- The full curriculum — 5 modules, 32 lessons
- An AI coach that knows this curriculum and your business
- Progress tracking across the curriculum
No videos, no written lesson pages, no quizzes, no certificate. You work through the curriculum in conversation with the coach.
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What You'll Learn
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
- -API accounts on ElevenLabs and Deepgram (free tiers available). Estimated API costs for course projects: $15-30.
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
- 1.2Your first voice app: text in, speech out in 10 minutes
- 1.3Voice AI architecture: input, processing, response, and output
- 1.4Key providers compared: ElevenLabs, OpenAI, Deepgram, AssemblyAI, PlayHT
- 1.5Setting up your voice AI development environment
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
- 5.7Voice AI Project Portfolio — package, demo, and deploy your voice applications
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