Liquid AI Open-Sources 74M-Parameter Streaming Audio Codec

Liquid AI released LiquidCodec, a ~74M-parameter streaming audio codec that converts speech into tokens a language model can predict and back into audio, powering LFM3-Audio-2B and LFM3-Omni-2B. Its key innovation replaces residual vector quantization with grouped finite scalar quantization (GFSQ), producing 12 independent tokens per 80 ms frame that can be predicted in parallel, while the decoder predicts spectral coefficients directly and skips transposed-convolution upsampling. On a single CPU thread of an Apple M4 Max (FP32) it encodes at ~361x and decodes at ~87x real time, with an encode RTF of 0.0028 versus 0.0361 for Kyutai's Mimi, and posts the best ViSQOL and TTS UTMOSv2 across all 8 test sets; however, PESQ still trails NVIDIA's NanoCodec on 7 of 8 sets, and no bitrate or latency in milliseconds was published. Weights are on Hugging Face with GGUF builds for llama.cpp.