LFM2 Audio Setup: Install & Test the End-to-End Model

This guide walks through my install and first test of LFM2 Audio, an end-to-end audio model from Liquid AI. I cover what it is, how it differs from other audio models, what to expect in practice, and how to get it running locally.

My focus is on a clean setup and quick validation. I also share observations on memory use, latency, and voice activity behavior so you know what to expect before you run it.

What is LFM2 Audio?

LFM2 Audio is a 1.5B-parameter model that understands and generates both text and audio. It builds on the LFM series and extends prior work (including the 1.2B LFM) with audio in and audio out.

A notable design choice is the disentangling of audio input and audio output representations. In plain terms, the model separates how it processes incoming audio from how it produces outgoing audio. This separation aims to improve control and stability across tasks that involve listening and speaking.

Overview of LFM2 Audio

Aspect	Details
Developer	Liquid AI
Model size	1.5B parameters
Modalities	Text understanding and generation, audio understanding and generation
Architecture note	Extends the LFM series (including LFM 1.2B); disentangles audio input/output representations
Claimed strengths	Competitive results on VoiceBench; focus on inference speed
Observed behavior	Consumes a bit over 5 GB of VRAM; not as fast in my test as claimed; detects voice pauses; output sounds somewhat robotic
Hardware used	Single GPU with ~5 GB VRAM available
OS during test	Ubuntu 22.04
Python version	Python 3.12
Local access	Web UI demo served on localhost
First-run behavior	Warms up a voice activity detector (VAD) before interaction

Key Features of LFM2 Audio

Multimodal: understands and generates both text and audio.
Disentangled audio pathways: separates audio input and output representations.
Competitive benchmarks: reports on VoiceBench show it outperforming larger models.
Local demo: runs a simple local web interface for quick testing.

Why disentangled audio pathways matter

Stable processing: separating how the model hears and how it speaks helps reduce cross-talk between tasks.
Better control: developers can reason about input and output behaviors independently.
Clearer iteration: audio in and audio out can be tuned separately without entangling both sides.

Benchmarks and Expectations

The team reports strong results on VoiceBench, beating models that are much larger. That aligns with the goal of a compact, capable model for audio understanding and response.

Inference speed is highlighted as a strength, though my hands-on test did not match those claims. Latency was noticeable, and responses felt a bit slower than hoped. Still, the model did detect pauses and voice activity, which is central to handling real-time input with a VAD.

Practical Observations From My Test

VRAM usage: a touch over 5 GB during interaction.
Latency: slower than expected; not ideal for rapid back-and-forth.
Voice activity: the VAD detected pauses and segmented input as intended.
Audio output quality: intelligible but robotic; I would prefer more natural prosody.

These are early impressions. There is room for improvement in speed and speech quality, but the core stack works.

System Requirements

Before installation, confirm the basics:

GPU: at least ~5 GB VRAM available for the model session.
OS: tested on Ubuntu 22.04.
Python: version 3.12.

If you are short on VRAM, close other GPU-heavy processes to free memory.

Install and Setup

Below is the same process I followed, condensed and organized.

Step-by-step installation

Confirm Python

Ensure Python 3.12 is installed and active in your environment.

Prepare your environment

Update packages and GPU drivers as needed.
Verify that your CUDA stack and GPU are visible to Python if you plan to run on GPU.

Install LFM2 Audio

Run the install command for the package (as shared by the project):
- Command: liquid audio

Start the local demo

Launch the web interface:
- Command: liquid audio demo

First run behavior

VAD warm-up: on first launch, the app warms up its voice activity detector. This prepares the model to detect pauses and segment your speech accurately.
Local access: once warm-up completes, the demo serves on your local machine. Open it in your browser to interact.

Using the demo

Microphone input: press Record, speak, and stop recording.
Model response: the system processes your audio, generates a response, and may also output audio.

In my run, I saw steady VRAM use, evident pause detection, but slower responses and robotic speech output.

Setup Notes

Memory: expect a bit over 5 GB VRAM usage when the session is active.
Cold start: first interaction is slower due to initial model and VAD warm-up.
OS: the steps above were done on Ubuntu 22.04.
Python: the demo assumes Python 3.12; mismatched versions can cause issues.

Performance and Quality

Here’s how the experience stacked up in practice.

Speed

The model responded, but not as quickly as I hoped based on the stated focus on speed.
Warm-up and first response took longer; subsequent turns were still on the slower side.

Voice Activity Detection

Pauses and speech segments were detected.
This helped the system know when to process audio and when to wait.

Audio Output

Speech synthesis sounded robotic in my test.
Intelligibility was fine, but naturalness needs work.

What Stood Out

Three things stood out: a modest footprint for a multimodal audio model, the architectural choice to disentangle audio input and output, and competitive benchmark claims versus larger models.

On the flip side, speed and speech naturalness fell short of expectations in my test. It’s a workable starting point for local experiments, with clear areas to improve.

Installation Recap

Confirm Python 3.12.
Have ~5 GB free VRAM.
Install the package via the provided command.
Launch the demo with liquid audio demo.
Wait for VAD warm-up, then access locally and start testing.

Troubleshooting Pointers

If the demo does not start or respond:

Check Python version: ensure 3.12 is active.
Check GPU memory: confirm enough VRAM is free.
Wait out warm-up: first launch can take a bit while the VAD initializes.
Restart the demo: close and relaunch if the session hangs.

Practical Use Cases

LFM2 Audio has clear potential for tasks that need both listening and speaking:

Audio assistants that process speech and respond in audio or text.
Interactive tools that need pause detection and voice-turn awareness.
Local experiments where a compact model can run on a single GPU.

The current output quality and speed suggest it is still early for polished, production-grade voice experiences, but the core behaviors are in place for prototyping.

Notes on Architecture

The key idea is separating the pathways for audio input and audio output. This can help the model listen without entangling generation behavior, and speak without muddling the input side. It extends work from the LFM line (including LFM 1.2B) and adds a multimodal interface that supports both directions.

That structure likely supports the reported benchmark strength on VoiceBench, where LFM2 Audio was said to outperform much larger models. Size is not the only factor here; the design of the audio stack appears to matter.

Benchmark Context

On VoiceBench, the model reportedly beats larger peers. Benchmarks are only one part of the story, but they suggest the model is competitive for tasks the suite measures.

In my setup, speed did not match the highlight, but the model handled audio turns and pause detection well. Results will vary by machine, build, and background load.

Resource Profile

VRAM: just over 5 GB in usage during interaction.
CPU: light to moderate overhead depending on the browser and recording pipeline.
Disk: not a concern in my test beyond typical Python environments.

If you have a smaller GPU, close unused apps to free VRAM before launching the demo.

Running Locally: Workflow

Launch the demo with liquid audio demo.
Wait for the VAD warm-up to complete.
Open the local URL in your browser.
Record a short utterance, stop, and wait for the model response.
Repeat with a few turns to gauge latency and stability.

Keep early tests short. This helps you spot issues with timing and VAD behavior without long waits.

What I Would Watch Next

Speech naturalness: monitor updates that improve prosody and pacing.
Latency: look for optimizations that reduce response time.
Memory use: see if future releases cut VRAM requirements further.

These changes would improve the day-to-day user experience for local voice interactions.

Summary

LFM2 Audio is a 1.5B multimodal model from Liquid AI that handles text and audio in both directions. It extends the LFM series and splits the audio stack into distinct input and output sides. On VoiceBench, it reportedly outperforms much larger models.

In practice, my local run used a bit over 5 GB VRAM, showed reliable voice activity detection, but felt slower than expected and produced robotic speech. Setup was straightforward: confirm Python 3.12, run the install command, and start the demo with liquid audio demo. The first launch warms up a voice activity detector before the interface becomes responsive.

It’s an interesting start for local audio experiments. If you want a compact model that can listen and speak, and you have a GPU with around 5 GB free VRAM, it is worth a try. With improvements in speed and output naturalness, it could become a stronger option for on-device audio workflows.

Quick Reference

Requirements

GPU with ~5 GB free VRAM
Ubuntu 22.04 (tested)
Python 3.12

Commands

Install: liquid audio
Launch demo: liquid audio demo

First Run

VAD warm-up occurs before interaction.
Access locally in your browser after the server starts.

Observations

VRAM: just over 5 GB during interaction
Speed: slower than expected in my test
Audio: intelligible but robotic
VAD: detects pauses and segments speech

Final Thoughts

If your goal is to experiment with end-to-end audio on a single GPU, LFM2 Audio offers a workable path with local control. The install is brief, the demo is accessible, and the design choices are sensible for audio pipelines. Keep expectations measured on speed and speech quality for now, and watch for updates that address both.