Sesame's CSM uses a novel transformer architecture optimized for conversational speech generation. Unlike traditional TTS systems that operate on isolated utterances, CSM maintains conversational context across multi-turn dialogues, enabling natural flow and coherent responses.

The model employs a hierarchical attention mechanism that captures both local (phoneme-level) and global (discourse-level) features, allowing for contextually appropriate prosody, timing, and emotional expression.

• 24-layer transformer with 512M parameters optimized for speech synthesis
• Multi-head attention mechanisms for prosodic feature extraction
• Contextual embedding layer for emotional state tracking
• Adversarial training for natural speech rhythm and timing

The CSM uses an amortized inference approach that dramatically reduces latency while maintaining high-fidelity output. Traditional diffusion-based speech models require hundreds of denoising steps; Sesame's approach achieves comparable quality in a single forward pass.

This breakthrough enables real-time conversational AI with latency under 200ms—critical for natural clinical dialogue where delayed responses break immersion and reduce training effectiveness.

• Single-step inference with 180ms average latency
• Consistency distillation for quality-preserving compression
• GPU-optimized inference pipeline for scalability
• Dynamic batching for efficient resource utilization