CF

Build Voice Agents From Scratch

17h 8m 50s
English
Paid

Build Voice Agents From Scratch is a 11-lesson 17 hours 8 minutes self-paced course by Dr. Sreedath Panat, Vizuara AI Labs. Voice is becoming the new standard for interacting with AI.

Course facts

Lessons
11
Duration
17 hours 8 minutes
Language
English
Instructor
Dr. Sreedath Panat, Vizuara AI Labs
Price
Premium

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.

Who teaches Build Voice Agents From Scratch?

Dr. Sreedath Panat

Dr. Sreedath Panat thumbnail

Dr. Sreedath Panat — a research engineer and entrepreneur known for his developments in AI and sustainable technologies:

  • He holds a PhD from Massachusetts Institute of Technology (MIT), where he studied applied methods of mechanics, machine learning, and artificial intelligence.
  • He graduated from IIT Madras (dual degree BTech) before enrolling in MIT.
  • He co-founded Vizuara AI Labs, where he serves as an engineer and AI product strategist.
  • He is known as the inventor of self-cleaning AI-powered solar technology — a development that uses intelligent systems to optimize the cleaning and efficiency of solar panels.
  • In addition to engineering work, he actively participates in the development of educational programs on AI, conducts technical lectures, and shares practical knowledge on ML and computer vision.

Vizuara AI Labs

Vizuara AI Labs thumbnail

Vizuara is an international educational school in the field of artificial intelligence, founded by graduates of IIT Madras, MIT, and Purdue University. The company is developing with the support of the MIT ecosystem and aims to make quality AI education accessible to students and professionals worldwide.

Learning at Vizuara is based on the principle of "learn by creating": instead of studying theory alone, students work on real projects in the fields of artificial intelligence, machine learning, and generative AI. The programs combine fundamental knowledge, practical assignments, individual mentoring, and constant feedback, helping to quickly acquire in-demand skills.

In addition to technical courses, the school supports the development of academic and professional profiles: it assists with preparing portfolios, research papers, capstone projects, applying to foreign universities, as well as interview preparation and starting a career in AI.

The mission of Vizuara is to democratize access to modern education in the field of artificial intelligence by providing structured programs, practical experience, and mentorship. The school aims to become one of the most reliable educational platforms in emerging markets, where students not only gain knowledge but also create projects, build strong portfolios, and receive real career opportunities.

The philosophy of Vizuara is based on high-quality standards, a practical approach to learning, transparency, honest feedback, and a focus on the success of each student.

What lessons are included in Build Voice Agents From Scratch?

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#1: 001 Session 1 - Voice agent foundations
All Course Lessons (11)
#Lesson TitleDurationAccess
1
001 Session 1 - Voice agent foundations Demo
01:37:34
2
002 Session 2 - ASR in depth
02:06:51
3
003 Transformers foundations
01:15:34
4
004 Session 3 - TTS in depth
02:07:17
5
005 Session 4 - LLM the brainSession 4 - LLM the brain
01:58:07
6
006 Session 5 - Tools and memory for the LLM reasoning layer
01:46:08
7
007 Session 6 - Streaming, buffer and barge-in
01:43:34
8
008 Session 7 - Building the full voice agent from scratch
02:27:18
9
009 Session 8 - Reducing latency, Speech-to-speech models, livekit and VAPI
01:23:13
10
010 How can you start and sustain
15:08
11
011 My philosophy
28:06
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Frequently asked questions

What knowledge should I have before enrolling?
The course recommends confident proficiency in Python and a basic understanding of machine learning principles. Prior experience with voice technologies is not required. The lesson sequence includes Voice agent foundations and Transformers foundations before moving through speech recognition, speech synthesis, and LLM-based reasoning. These foundations provide context for the later integration topics, but the stated Python recommendation means the course should not be treated as an introduction to programming. No more specific prerequisite libraries or mathematical requirements are listed.
What will I build during the course?
The stated learning goal is a complete voice-agent pipeline, connecting audio acquisition to voice response generation through Speech-to-Text (ASR), a Large Language Model (LLM), and Text-to-Speech (TTS). The lesson Building the full voice agent from scratch explicitly focuses on integrating the agent. Supporting lessons address Tools and memory for the LLM reasoning layer and Streaming, buffer and barge-in. The supplied material does not identify a particular application scenario, such as customer support, or specify a final deployment environment.
Which kinds of students and professionals is the course intended for?
The description identifies Python developers moving into Voice AI, ML and AI engineers interested in speech technologies, backend developers building intelligent services, and engineers studying LLM and agent systems. Researchers and students interested in modern speech processing are also included. The curriculum connects these interests through ASR, TTS, LLM reasoning, tools and memory, and full-agent integration. Enrollment does not require previous voice-technology experience, although the recommended Python proficiency and machine learning background still apply across these audience groups.
How does the scope differ from a course focused only on speech recognition or LLMs?
The listed curriculum spans the voice interaction pipeline rather than stopping at transcription or language-model responses. ASR in depth and TTS in depth address the speech components, while LLM the brain and Tools and memory for the LLM reasoning layer cover the reasoning layer. Streaming, buffer and barge-in introduces interaction handling, followed by full-agent construction and latency reduction. Transformers foundations is also included. The material establishes this breadth, but it does not provide enough detail to compare technical depth with a particular competing course.
Are LiveKit, VAPI, and speech-to-speech models included?
Yes. The lesson Reducing latency, Speech-to-speech models, livekit and VAPI explicitly names all three alongside latency reduction. They appear after Building the full voice agent from scratch, placing them later in the listed sequence than the ASR, TTS, and LLM lessons. The supplied outline does not specify which LiveKit or VAPI features are demonstrated, whether either platform is required for the main build, or which speech-to-speech models are used. It also does not state platform pricing or account requirements.
Does the outline include deployment, security, or custom speech-model training?
The supplied lesson list does not explicitly identify deployment targets, security practices, or training custom speech models. Its named topics are voice-agent foundations, ASR, transformers, TTS, LLM reasoning, tools and memory, streaming and barge-in, full-agent construction, and latency reduction with speech-to-speech models, LiveKit, and VAPI. This does not establish that the unlisted topics never arise, but they should not be assumed to be dedicated parts of the course. Likewise, building an agent from scratch does not establish that its underlying models are trained from scratch.
How much time should I set aside to complete the course?
The catalogue lists 11 lessons, but it does not provide a usable total duration: the runtime field is 00:00:00. Individual lesson lengths and expected coding time are also absent, so a reliable completion estimate cannot be calculated from the supplied material. The sequence includes foundational topics, component-specific lessons, Building the full voice agent from scratch, and a later lesson on latency reduction and platforms. How can you start and sustain and My philosophy are also included in the lesson count.