LocalVQE

Local Voice Quality Enhancement — compact neural models for acoustic echo cancellation (AEC), noise suppression (NS), and dereverberation of 16 kHz speech, running on commodity CPUs in real time. Causal and streaming (256-sample hop, 16 ms latency).

• Try it:
• Source, build system, tests:

This page hosts the published weights. Inference runs the GGML C++ engine on the GGUF files directly (build instructions on GitHub).

Authors: Richard Palethorpe (richiejp) and Claude (Anthropic). LocalVQE is a streaming, CPU-tuned derivative of DeepVQE (Indenbom et al., Interspeech 2023).

Models

Speed is per 16 ms hop on a Ryzen 9 7900 (Zen4), 4 threads; RT = realtime factor (higher is faster than realtime).

• Joint models (v1.2 / v1.3) clean echo, noise, and reverb in one pass. v1.3 is wider and filters noise better; v1.2 is ~1/4 the per-hop cost.
• v1.4-AEC removes only the far-end echo and passes voice, room, and background through unchanged. It's a classical adaptive filter followed by a small neural mask. The 2.7K build is that filter alone — cheaper and gentler, but it can't remove heavily reverberant echo the way the mask can.
• Every model needs a far-end reference signal (a loopback of what your speakers play) in addition to the mic.
• bf16 GGUFs are ~12 % smaller with identical quality and speed; pick f32 unless download size matters.

Compact line — GTCRN-AEC (for lower-power CPUs)

A separate, much smaller second line of models for lower-power CPUs: a ~49 K-parameter GTCRN-AEC network — a distinct architecture based on GTCRN (Rong et al., ICASSP 2024) — paired with the project's DSP echo-cancellation front-end. The GGUFs are self-contained, so they run with the same single command as every other model. Two variants share the architecture:

Whole-clip real-time factor on the real ggml graph, benchmarked on a Raspberry Pi 5 (one example of a low-power target; test_gtcrn --bench, Cortex-A76, Ubuntu 24.04), parity-verified to the PyTorch reference within ~1e-6 on-device. RTF is identical for both variants:

That is ~0.78 ms per 16 ms hop single-threaded. Runs on any CPU; for single-board ARM, cross-compile for aarch64 with ggml/docker/Dockerfile.arm64 (docker buildx

• qemu). f16/q8 quantizations are published only if/when released.

Files in this repository

v1.4-AEC is GGUF-only (no .pt). GGUF integrity is checked at load time against a built-in SHA256 allowlist in the engine.

Performance

Full 800-clip eval on the ICASSP 2022 AEC Challenge blind test set (real recordings). AECMOS echo / deg are 1–5 (higher = more echo removed / cleaner speech); blind ERLE is 10·log10(E[mic²]/E[enh²]), only meaningful on far-end-only clips. Unprocessed-mic echo MOS is 2.67 / 2.56 / 1.90 / 2.13 / 5.00 across the five scenarios.

v1.4-AEC — keeps background noise and room by design, so its ERLE and far-end DNSMOS are intentionally lower than the joint models (it isn't deleting the ambience):

v1.4-AEC 2.7K (front-end only) — matches or beats the full model's perceptual far-end echo at 1/74 the parameters; the mask's extra work shows up as higher ERLE above, not higher echo MOS: