specific method · filed under model architecture
Universal Speech Model (USM)-based audio encoder
An audio encoder based on USM, with two downsampling convolution layers followed by twelve Conformer layers.
Also called Universal Speech Model-based audio encoder, USM-based audio encoder.
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The encoder architecture is based on the Universal Speech Model [Zhang et al., 2023, USM], consisting of two downsampling convolution layers followed by twelve Conformer layers [Gulati et al., 2020].
usedmodel architecturein Gemma 4Google DeepMind
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Other methods under model architecture :: multimodal architecture.
Encoder-free multimodal architectureEarly fusion multimodal trainingDiscrete token encoding for audioHierarchical patch encoderMixed-modality trainingConfigurable visual token budgetJoint multimodal decoding in a shared hidden spaceNative multimodal understanding3×3 pixel-unshuffle downsamplingJoint vision-language pre-trainingRaw audio projection into the LLM embedding spaceVision Transformer (ViT)2×2 pixel-shuffle downsamplingAudio encoder initialized from MiMo-AudioAudio Transformer (AuT)Causal streaming ConvNet codec decoderData-parallel-first multimodal encodingDecoupled Encoder Process (DEP)Dedicated multimodal encodersDisaggregated encoder trainingDiscarding the LLM after vision-encoder trainingDual-format coordinate supervisionExplicit text-string timestampsFour-frame audio patches with within-patch bidirectional self-attention