Sesame AI and RVQs - The Network Architecture Behind Viral Speech Models

Overview

Explore the architecture of Sesame Conversational Speech Model, including Mimi Encoder tokenization with split RVQ, semantic and acoustic codes, and the Autoregressive Transformer Backbone that enables natural speech interaction.

Syllabus

- Introduction to Conversational Speech Models -- Overview of Conversational AI -- Importance of Speech Models in Modern AI - Sesame Conversational Speech Model Architecture -- General Structure and Functionality -- Key Components Overview - Mimi Encoder and Tokenization -- Concept of Mimi Encoder -- Tokenization Process -- Advantages of Mimi Encoding - Split Residual Vector Quantization (RVQ) -- Fundamentals of RVQ -- Split RVQ Technique -- Role in Speech Model - Semantic and Acoustic Codes -- Explanation of Semantic Codes -- Explanation of Acoustic Codes -- Integration within the Model - Autoregressive Transformer Backbone -- Overview of Autoregressive Models -- Transformer Architecture in Speech Models -- Benefits for Natural Speech Interaction - Applications of Sesame AI -- Real-world Use Cases -- Future Trends and Opportunities - Practical Implementation and Case Studies -- Hands-on Sessions -- Analysis of Successful Use Cases

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