Mastering Generative Voice AI From Tokens to Agentic TTSPublished 7/2026
Created by Vinit Singh
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate |
Genre: eLearning |
Language: English |
Duration: 374 Lectures ( 33h 1m ) |
Size: 19.7 GB
Master SpeechLMs, neural audio codecs, diffusion & flow matching to build real-time agentic voice AI systems⚡ Explain the physics, phonetics, and acoustic features that underlie human speech production
⚡ Compare traditional cascade TTS pipelines with modern Speech Language Model (SpeechLM) architectures
⚡ Build and apply neural audio codecs and semantic tokenization (EnCodec, HuBERT, wav2vec 2.0, RVQ) (
⚡ Implement autoregressive codec-based TTS with multi-stream token decoding strategies
⚡ Design unified speech-text models with cross-modal alignment and paralinguistic control
⚡ Apply latent diffusion and conditional flow matching to generate high-quality mel-spectrograms
⚡ Evaluate flow matching vs. diffusion trade-offs for speed, quality, and controllability
⚡ Deploy low-latency, streaming agentic TTS systems with real-time interruption handling
Requirements❗ A solid understanding of deep learning fundamentals (neural networks, backpropagation, and training basics)
❗ Working knowledge of Python and a deep learning framework such as PyTorch
❗ Basic familiarity with core NLP or LLM concepts (tokenization, transformers, attention) is helpful but not mandatory - key ideas are reviewed in the course
❗ No prior audio signal processing experience needed - Module 1 builds this from first principles
DescriptionGenerative voice AI has moved far beyond simple text-to-speech - and this course takes you from the physics of sound all the way to building production-grade, agentic voice systems.Most TTS courses stop at basic vocoders or off-the-shelf APIs. This one goes deeper. You'll start with the
fundamentals of human speech - acoustics, phonetics, and prosody - before diving into the architectures actually powering today's state-of-the-art voice models: self-supervised representation learning (wav2vec 2.0, HuBERT), neural audio codecs (EnCodec, SoundStream, DAC), and the tokenization strategies that let LLMs "speak."
From there, you'll master the two dominant modern paradigms -
autoregressive codec-based TTS and
latent diffusion / conditional flow matching - understanding exactly when and why each is used in real systems. You'll also explore unified speech-text models, paralinguistic modeling (laughter, breathing, affect), and zero-shot voice cloning.
By the final module, you'll understand how to build
low-latency, streaming, agentic voice pipelines - the same techniques behind real-time conversational AI agents - covering chunked inference, speculative decoding, WebSocket streaming, and turn-taking.
What you'll learn✨ The science of speech production and acoustic feature extraction
✨ How neural audio codecs and semantic tokenization work
✨ Autoregressive and diffusion/flow-based TTS architectures
✨ Cross-modal speech-text alignment techniques
✨ Building low-latency, interruption-aware conversational voice agents
Whether you're an ML engineer, researcher, or voice-tech founder, this course gives you the
complete architectural picture - from tokens to agents.
Who this course is for⭐ ML/AI engineers who want to move beyond calling TTS APIs and understand how state-of-the-art voice models actually work under the hood
⭐ Speech and NLP researchers looking to bridge classical signal processing with modern generative modeling (diffusion, flow matching, SpeechLMs)
⭐ Voice-tech founders and product engineers building conversational AI agents who need to make informed architecture decisions
⭐ Graduate students or self-taught ML practitioners seeking a rigorous, end-to-end curriculum on generative audio, from acoustic theory to agentic deployment
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