Generative Audio Models Explained: Neural Codecs, Transformers, Diffusion & Flow Matching

Generative audio is evolving fast. Modern systems no longer work directly with massive raw waveforms. Instead, they use neural codecs like Encodec and DAC to compress sound into discrete tokens that transformers, diffusion models, and flow matching architectures can generate efficiently.

In this video, we break down the core architectures behind AI speech, music, and sound effect generation. You’ll learn how autoregressive models power low-latency streaming text-to-speech, why diffusion models improve audio fidelity, and why flow matching is becoming a leading paradigm for high-quality music generation.

We also explore the real production challenges behind generative audio, including voice cloning ethics, long-term musical structure, latency, fidelity, and deployment trade-offs.

This is a practical technical guide for AI engineers, researchers, builders, and creators who want to understand how modern generative audio systems work.

Topics Covered
↳ Neural codecs: Encodec, DAC, and audio tokenization
↳ Autoregressive audio generation
↳ Diffusion models for speech, music, and sound effects
↳ Flow matching for high-fidelity music generation
↳ Real-time TTS vs batch audio generation
↳ Voice cloning risks and responsible AI
↳ Production trade-offs in generative audio systems

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Видео Generative Audio Models Explained: Neural Codecs, Transformers, Diffusion & Flow Matching канала Engineering Insider