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Sesame CSM: contextual speech generation with amortized audio decoding

Contribution grade

Contextual speech-model systems · Human-designed research method or resource

Reference score60,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Modeled interleaved text/audio history with a zeroth-codebook backbone and smaller residual-codebook decoder, subsampling decoder training frames and evaluating contextual pronunciation/prosody.

Scientific scope

Modeled interleaved text/audio history with a zeroth-codebook backbone and smaller residual-codebook decoder, subsampling decoder training frames and evaluating contextual pronunciation/prosody. Inherits RQTransformer, Llama-style architecture and Mimi codec from Moshi. Acquired delta is zeroth-codebook backbone/small decoder split, decoder-frame subsampling and contextual evaluation integration, not first hierarchical audio transformer. Technical post admits deficient conversational structure and a context-sensitive human-prosody gap.

Evidence

The linked original sources describe this contribution and its evidence limits. WRS has not independently reconstructed the full scientific claims. Inherits RQTransformer, Llama-style architecture and Mimi codec from Moshi. Acquired delta is zeroth-codebook backbone/small decoder split, decoder-frame subsampling and contextual evaluation integration, not first hierarchical audio transformer. Technical post admits deficient conversational structure and a context-sensitive human-prosody gap.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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