LMCodec: A Low Bitrate Speech Codec With Causal Transformer Models

Teerapat Jenrungrot1, Michael Chinen2, W. Bastiaan Kleijn2,3, Jan Skoglund2, Zalán Borsos2, Neil Zeghidour2, Marco Tagliasacchi2

1 University of Washington, Seattle
2 Google
3 School of Engineering and Computer Science, Victoria University of Wellington


We introduce LMCodec, a causal neural speech codec that provides high quality audio at very low bitrates. The backbone of the system is a causal convolutional codec that encodes audio into a hierarchy of coarse-to-fine tokens using residual vector quantization. LMCodec trains a Transformer language model to predict the fine tokens from the coarse ones in a generative fashion, allowing for the transmission of fewer codes. A second Transformer predicts the uncertainty of the next codes given the past transmitted codes, and is used to perform conditional entropy coding. A MUSHRA subjective test was conducted and shows that the quality is comparable to reference codecs at higher bitrates.


Audio Examples