Liquid AI Releases LiquidCodec, a 74M-Parameter Streaming Audio Codec

Liquid AI has released LiquidCodec, the audio codec behind its LFM3-Audio-2B and LFM3-Omni-2B models.

The LiquidCodec audio codec is a ~74M-parameter streaming speech tokenizer.

It turns speech into tokens a language model can predict, then converts those tokens back into audio.

Liquid AI had 2 goals: easy-to-predict tokens and fast conversion back to speech.

The weights are on Hugging Face.

TL;DR Size: ~74M parameters (73.6M on the model card); 24 kHz audio, 12.5 Hz frames, 12 codebooks.

Runs on: a single CPU thread (tested on an Apple M4 Max laptop, FP32); CUDA GPUs via Transformers; Q4_0, Q4_K_M and Q8_0 GGUF builds for llama.cpp.

Performance: best ViSQOL and lowest mel distance on all 8 test sets versus X-Codec 2, NanoCodec and Mimi.

Best: ~361x real-time encoding on 1 CPU thread (encode RTF 0.0028 vs Mimi’s 0.0361).

What is LiquidCodec?

LiquidCodec is a Liquid AI speech codec that compresses 24 kHz audio into 12 token streams.

Each 80 ms frame becomes 12 discrete tokens, per the LFM3-Audio-2B model card.

It replaces Kyutai’s Mimi, which Liquid AI used in LFM2-Audio-1.5B.

Mimi has 96.2M parameters.

How does LiquidCodec work?

The pipeline is encode, quantize, decode.

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