---
title: LocalVQE Demo
emoji: 🎙️
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
python_version: "3.12"
pinned: false
license: apache-2.0
models:
  - LocalAI-io/LocalVQE
---

# LocalVQE Demo

Interactive demo for [LocalAI-io/LocalVQE](https://huggingface.co/LocalAI-io/LocalVQE) —
compact open-source models that clean up a microphone signal on CPU
in a single causal pass. The selector offers the joint models
(acoustic echo cancellation + noise suppression + dereverberation:
v1.3 at 4.8 M parameters, v1.2 at 1.3 M, plus older releases for
A/B), and **v1.4-AEC** (203 K parameters) which removes *only* the
echo — near-end speech, background noise, and room acoustics are
kept intact by design.

Pick one of the bundled examples or upload your own `(mic,
far-end-reference)` 16 kHz wav pair. For a pure noise-suppression
test, upload silence (or nothing) as the reference. With v1.4-AEC
selected, noise-only clips should come back nearly unchanged —
that's the intended behaviour.

Built with Gradio. Inference runs through the released **GGML C++
engine** (the same `liblocalvqe.so` production users build from the
repo) loading the published `.gguf` files — what you hear is the
deployed artifact, not a Python re-implementation. CPU-only; a
10-second clip takes well under a second on the Space's default tier.

Code: [github.com/localai-org/LocalVQE](https://github.com/localai-org/LocalVQE) ·
Training: [github.com/localai-org/LocalVQE-train](https://github.com/localai-org/LocalVQE-train).