Voice is becoming the new standard for interacting with AI. The largest tech companies—Meta, Google, Microsoft, NVIDIA, and leading venture funds—agree that voice interfaces will be the next stage in the development of artificial intelligence. The Conversational AI market is rapidly growing, investments in Voice AI are increasing every year, and the demand for specialists capable of creating intelligent voice assistants is one of the highest in the industry. This course will help you not only understand the technology but also learn how to create full-featured industrial-level voice AI agents using modern models, tools, and architectures.
Who this course is for
The program is designed for: - Python developers who want to transition into the Voice AI field; - ML and AI engineers who wish to work with speech technologies; - Backend developers creating intelligent services; - Engineers studying LLM and agent systems; - Researchers and students interested in modern speech processing technologies. Prior experience in voice technologies is not required, but confident proficiency in Python and a basic understanding of machine learning principles are recommended.
What you will learn
By the end of the course, you will be able to: - Design the architecture of modern voice AI agents; - Create a complete speech processing cycle: from audio acquisition to voice response generation; - Use Speech-to-Text (ASR), Large Language Models (LLM), and Text-to-Speech (TTS) in a unified pipeline; - Develop systems with minimal real-time data transmission delay; - Implement memory, external tool calling, and agent behavior; - Work with audio streaming through WebSockets; - Design scalable production-ready solutions; - Deploy voice assistants locally, in the browser, and in the cloud; - Create industry-level projects for your portfolio.
Course Program
Over 8 practical modules, you will sequentially build your own voice AI agent.
Module 1. Architecture of Voice AI Agents
- Difference between a voice agent and a chatbot; - Full speech processing cycle; - Streaming architecture; - Delays, interruption handling, and dialogue management; - Creating the first working voice pipeline.
Module 2. Speech-to-Text (ASR)
- Basics of automatic speech recognition; - Whisper, Faster-Whisper, and Distil-Whisper; - Recording audio from a microphone; - Voice Activity Detection (VAD); - Creating your own transcription system.
Module 3. Text-to-Speech (TTS)
- Modern speech synthesis technologies; - Comparing Piper, Coqui TTS, and ElevenLabs; - Generating natural speech; - Local and cloud solutions; - Voice streaming playback.
Module 4. LLM as the Intelligence of a Voice Agent
- Language model integration; - Dialogue building; - Context management; - Designing effective prompts for voice communication; - Creating natural user interaction.
Module 5. Tools, Memory, and Agent Systems
- Tool Calling; - Short-term and long-term memory; - Connecting APIs; - Information retrieval; - Performing actions by the voice agent.
Module 6. Real-time Voice Agents
- WebSockets; - Streamed speech processing; - Partial transcription; - Handling user interruptions (Barge-in); - Optimizing system operation speed.
Module 7. Production Architecture
- Designing reliable services; - Scaling; - Logging and monitoring; - Fault tolerance; - Cost optimization of model usage; - Comparing popular Voice AI frameworks.
Module 8. Final Project
Each student will create a fully functional voice AI agent that can be used in real projects. Several options to choose from: - AI receptionist; - Voice assistant for meetings; - Research AI assistant; - Personal Desktop Assistant; - Voice agent for calendar and task management.
Practical Tools
During your studies, you will work with the same technologies used in modern Voice AI products: - Python; - OpenAI Whisper; - Faster-Whisper; - Distil-Whisper; - Piper TTS; - Coqui TTS; - Silero VAD; - GPT and Claude; - WebSockets; - Modern libraries for streaming audio processing.
Research Package
Each course participant receives additional materials for developing their own research project: - A personalized research roadmap for 8 weeks; - A scientific article template on the chosen topic; - A selection of key scientific publications; - A ready project structure and starter code; - Recommendations for experiments and research development.
Learning Outcome
Upon course completion, you will gain not only a deep understanding of Voice AI architecture but also a production-level project that can be included in your professional portfolio. You will be able to independently create intelligent voice assistants, integrate modern language models into speech interfaces, design scalable systems, and confidently apply for positions such as Voice AI Engineer, Conversational AI Engineer, or AI Software Engineer.
Additional
This package combines the main 8-day course on creating voice AI agents with the Research Starter Kit, providing comprehensive preparation for both practical development and the commencement of scientific work in the field of Voice AI. In addition to full access to the course program, you will receive recordings of all sessions, educational materials, presentations, project source code, and lifetime access to the materials. The package also includes a personalized research roadmap, a draft scientific article on the chosen topic, a carefully selected collection of scientific publications, and a ready project template with starter code. This learning format is suitable for developers, engineers, students, and researchers who want not only to learn to create modern voice AI systems but also to advance in the scientific field: publishing research, preparing for admission to master's or doctoral programs, and building a career in the field of Voice AI and conversational artificial intelligence. The combination of the course and research package allows you to go from mastering practical skills in developing voice AI agents to forming your own research topic and preparing the foundation for your first scientific publication—all within one educational program.
