LocalAI/docs/content/features/audio-transform.md

+++ disableToc = false title = "Audio Transform" weight = 34 url = "/features/audio-transform/" +++

The audio-transform endpoints take audio in and emit audio out, optionally conditioned on a second reference audio signal. The category is generic by design - concrete operations include joint acoustic echo cancellation + noise suppression + dereverberation (LocalVQE), voice conversion (reference = target speaker), pitch shifting, audio super-resolution, and so on.

The first shipping backend is LocalVQE, a 1.3 M-parameter GGML-based model that performs joint AEC + noise suppression

• dereverberation on 16 kHz mono speech, ~9.6× realtime on a desktop CPU. It is a derivative of the Microsoft DeepVQE paper.

The mental model

Every audio-transform request carries:

• audio - the primary input file (required).
• reference - an auxiliary signal whose meaning is backend-specific (optional).
  • For echo cancellation: the loopback / far-end signal played through the speakers.
  • For voice conversion: the target speaker's reference clip.
  • For pitch / style transfer: a tonal or style reference.
  • When omitted, the backend treats it as silence and degrades gracefully (LocalVQE, for example, does denoise + dereverb only when ref is empty).
• params - a generic key=value map forwarded to the backend.
  • LocalVQE keys: noise_gate=true|false, noise_gate_threshold_dbfs=<float>.

This shape mirrors WebRTC's ProcessStream(near) / ProcessReverseStream(far) APM API, NVIDIA Maxine's NvAFX_Run paired-stream signature, and the ICASSP AEC challenge 2-channel WAV convention.

Batch endpoint

POST /audio/transformations (alias POST /audio/transform) - multipart form-data, returns audio bytes.

Field	Type	Required	Notes
model	string	yes	Audio-transform model id (e.g. localvqe-v1.3-4.8m)
audio	file	yes	Primary input audio
reference	file	no	Optional auxiliary signal
response_format	string	no	wav (default), mp3, ogg, flac
sample_rate	int	no	Desired output sample rate
params[<key>]	string	no	Repeated; forwarded to backend

First install an audio-transform model from the gallery (the examples below use localvqe-v1.3-4.8m):

local-ai run localvqe-v1.3-4.8m

Example (LocalVQE: cancel echo, suppress noise, gate residual):

curl -X POST http://localhost:8080/audio/transformations \
  -F model=localvqe-v1.3-4.8m \
  -F audio=@mic.wav \
  -F reference=@loopback.wav \
  -F 'params[noise_gate]=true' \
  -F 'params[noise_gate_threshold_dbfs]=-50' \
  -o enhanced.wav

When reference is omitted, LocalVQE zero-fills the reference channel and the operation reduces to noise suppression + dereverberation.

Streaming endpoint

GET /audio/transformations/stream - bidirectional WebSocket. The first client message is a JSON envelope; subsequent client messages are binary PCM frames; server emits binary PCM frames at the same cadence.

Wire format

Client → server (text frame, first):

{
  "type": "session.update",
  "model": "localvqe-v1.3-4.8m",
  "sample_format": "S16_LE",
  "sample_rate": 16000,
  "frame_samples": 256,
  "params": { "noise_gate": "true" }
}

sample_format is S16_LE (16-bit signed little-endian) or F32_LE (32-bit float little-endian, [-1, 1]). frame_samples defaults to the backend's preferred hop length (256 = 16 ms for LocalVQE).

Client → server (binary frames, subsequent): interleaved stereo PCM, channel 0 = audio (mic), channel 1 = reference. Frame size: frame_samples × 2 channels × sample_size. For S16_LE at 256 samples that is 1024 bytes per frame; for F32_LE it is 2048 bytes. If the reference is silent (no auxiliary signal), send zeros on channel 1.

Server → client (binary frames): mono PCM in the same format, frame_samples × sample_size bytes (512 bytes for S16_LE, 1024 for F32_LE).

Mid-stream control (text frame): another session.update resets the streaming state when its reset field is true; a session.close text frame ends the session cleanly.

Latency

LocalVQE has 16 ms algorithmic latency (one hop). At runtime the per-frame CPU cost depends on the model: ~1.6 ms for the compact 1.3 M models (v1.1/v1.2, ~9.7× realtime) and ~3.3 ms for the wider v1.3 4.8 M model (~4.7× realtime) on a 4-thread modern desktop, leaving the rest of the budget for network and downstream playback.

Backend-specific tuning (LocalVQE)

params[<key>]	Type	Default	Effect
noise_gate	bool	false	Enable post-OLA RMS-based residual-echo gate
noise_gate_threshold_dbfs	float	-45.0	Gate threshold in dBFS; frames below are zeroed

The gate is most useful in far-end-only / silent-near-end stretches where the model's residual would otherwise sound like buffering or amplified noise floor. A reasonable starting point is -50 dBFS.

Configuring a model

LocalVQE ships several weight releases in the gallery: localvqe-v1.3-4.8m (current default - best quality), localvqe-v1.2-1.3m and localvqe-v1.1-1.3m (compact, ~¼ the per-hop cost - good for low-core or power-constrained hosts). All share the same backend and request API; only the model filename differs.

name: localvqe
backend: localvqe
parameters:
  model: localvqe-v1.3-4.8M-f32.gguf

# Backend-specific defaults can be set in Options[]; per-request
# params[*] form fields override.
#
# `backend` and `device` route through the upstream localvqe options
# builder so you can force a non-default GGML backend (e.g. `Vulkan`) or
# pin to a specific GPU index. Leave both unset to keep the CPU default.
options:
- noise_gate=true
- noise_gate_threshold_dbfs=-50
# - backend=Vulkan
# - device=0

See also

• [Text to Audio (TTS)]({{< relref "text-to-audio.md" >}})
• [Audio to Text]({{< relref "audio-to-text.md" >}})
• LocalVQE upstream
• DeepVQE paper (Indenbom et al., Interspeech 2023)