Liquid AI has released LFM2-Audio-1.5B, a compact audioālanguage foundation model that both understands and generates speech and text through a single end-to-end stack. It positions itself for low-latency, real-time assistants on resource-constrained devices, extending the LFM2 family into audio while retaining a small footprint.
But whatās actually new? a unified backbone with disentangled audio I/O
LFM2-Audio extends the 1.2B-parameter LFM2 language backbone to treat audio and text as first-class sequence tokens. Crucially, the model disentangles audio representations: inputs are continuous embeddings projected directly from raw waveform chunks (~80 ms), while outputs are discrete audio codes. This avoids discretization artifacts on the input path while keeping training and generation autoregressive for both modalities on the output path.
On the implementation side, the released checkpoint uses:
- Backbone: LFM2 (hybrid conv + attention), 1.2B params (LM only)
- Audio encoder: FastConformer (~115M, canary-180m-flash)
- Audio decoder: RQ-Transformer predicting discrete Mimi codec tokens (8 codebooks)
- Context: 32,768 tokens; vocab: 65,536 (text) / 2049Ć8 (audio)
- Precision: bfloat16; license: LFM Open License v1.0; languages: English
Two generation modes for real-time agents
- Interleaved generation for live, speech-to-speech chat where the model alternates text and audio tokens to minimize perceived latency.
- Sequential generation for ASR/TTS (switching modalities turn-by-turn).
Liquid AI provides a Python package (liquid-audio) and a Gradio demo to reproduce these behaviors.
Latency: <100 ms to first audio
Liquid AI team reports end-to-end latency below 100 ms from a 4-second audio query to the first audible responseāa proxy for perceived responsiveness in interactive useāstating it is faster than models smaller than 1.5B parameters under their setup.
Benchmarks: VoiceBench and ASR results
On VoiceBenchāa suite of nine audio-assistant evaluationsāLiquid reports an overall score of 56.78 for LFM2-Audio-1.5B, with per-task numbers disclosed in the blogās chart (e.g., AlpacaEval 3.71, CommonEval 3.49, WildVoice 3.17). The Liquid AI team contrasts this result with larger models like Qwen2.5-Omni-3B and Moshi-7B in the same table. (VoiceBench is an external benchmark introduced in late 2024 for LLM-based voice assistants)
The model card on Hugging Face provides an additional VoiceBench table (with closely relatedābut not identicalāper-task values) and includes classic ASR WERs where LFM2-Audio matches or improves on Whisper-large-v3-turbo for some datasets despite being a generalist speechātext model. For example (lower is better): AMI 15.36 vs. 16.13 (Whisper-large-v3-turbo), LibriSpeech-clean 2.03 vs. 2.10.
Alright, but why does it really matter in voice AI trends?
Most āomniā stacks couple ASR ā LLM ā TTS, which adds latency and brittle interfaces. LFM2-Audioās single-backbone design with continuous input embeddings and discrete output codes reduces glue logic and allows interleaved decoding for early audio emission. For developers, this translates to simpler pipelines and faster perceived response times, while still supporting ASR, TTS, classification, and conversational agents from one model. Liquid AI provides code, demo entry points, and distribution via Hugging Face.
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