A transformer block with a convolution module inside. Attention for global context, convs for local acoustic detail.
Introduced inMay 2020Conformer: Convolution-augmented Transformer for Speech Recognition
The Conformer sandwiches a depthwise-convolution module between attention and feed-forward layers (in a macaron FFN-attention-conv-FFN pattern), usually with relative position encoding in the attention. Convolutions capture local spectral patterns that pure attention handles poorly, which made Conformers the dominant speech-encoder architecture (NVIDIA’s FastConformer here).
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